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Data mining and knowledge discovery in
databases have been attracting a significant
amount of research, industry, and media atten-
tion of late. What is all the excitement about?
This article provides an overview of this emerging
field, clarifying how data mining and knowledge
discovery in databases are related both to each
other and to related fields, such as machine
learning, statistics, and databases. The article
mentions particular real-world applications,
specific data-mining techniques, challenges in-
volved in real-world applications of knowledge
discovery, and current and future research direc-
tions in the field.

cross a wide variety of fields, data are
being collected and accumulated at a
dramatic pace. There is an urgent need

for a new generation of computational theo-
ries and tools to assist humans in extracting
useful information (knowledge) from the
rapidly growing volumes of digital data.
These theories and tools are the subject of the
emerging field of knowledge discovery in
databases (KDD).

At an abstract level, the KDD field is con-

cerned with the development of methods and
techniques for making sense of data. The basic
problem addressed by the KDD process is one
of mapping low-level data (which are typically
too voluminous to understand and digest easi-
ly) into other forms that might be more com-
pact (for example, a short report), more ab-
stract (for example, a descriptive
approximation or model of the process that
generated the data), or more useful (for exam-
ple, a predictive model for estimating the val-
ue of future cases). At the core of the process is
the application of specific data-mining meth-
ods for pattern discovery and extraction.

This article begins by discussing the histori-

cal context of KDD and data mining and their
intersection with other related fields. A brief
summary of recent KDD real-world applica-
tions is provided. Definitions of KDD and da-
ta mining are provided, and the general mul-
tistep KDD process is outlined. This multistep
process has the application of data-mining al-
gorithms as one particular step in the process.
The data-mining step is discussed in more de-
tail in the context of specific data-mining al-
gorithms and their application. Real-world
practical application issues are also outlined.
Finally, the article enumerates challenges for
future research and development and in par-
ticular discusses potential opportunities for AI
technology in KDD systems.

Why Do We Need KDD?

The traditional method of turning data into
knowledge relies on manual analysis and in-
terpretation. For example, in the health-care
industry, it is common for specialists to peri-
odically analyze current trends and changes
in health-care data, say, on a quarterly basis.
The specialists then provide a report detailing
the analysis to the sponsoring health-care or-
ganization; this report becomes the basis for
future decision making and planning for
health-care management. In a totally differ-
ent type of application, planetary geologists
sift through remotely sensed images of plan-
ets and asteroids, carefully locating and cata-
loging such geologic objects of interest as im-
pact craters. Be it science, marketing, finance,
health care, retail, or any other field, the clas-
sical approach to data analysis relies funda-
mentally on one or more analysts becoming

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FALL 1996 37

From Data Mining to

Knowledge Discovery in

Databases

Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth

areas is astronomy. Here, a notable success
was achieved by

SKICAT

, a system used by as-

tronomers to perform image analysis,
classification, and cataloging of sky objects
from sky-survey images (Fayyad, Djorgovski,
and Weir 1996). In its first application, the
system was used to process the 3 terabytes
(10

bytes) of image data resulting from the

Second Palomar Observatory Sky Survey,
where it is estimated that on the order of 10

sky objects are detectable.

SKICAT

can outper-

form humans and traditional computational
techniques in classifying faint sky objects. See
Fayyad, Haussler, and Stolorz (1996) for a sur-
vey of scientific applications.

In business, main KDD application areas

includes marketing, finance (especially in-
vestment), fraud detection, manufacturing,
telecommunications, and Internet agents.

Marketing:

In marketing, the primary ap-

plication is database marketing systems,
which analyze customer databases to identify
different customer groups and forecast their
behavior. Business Week (Berry 1994) estimat-
ed that over half of all retailers are using or
planning to use database marketing, and
those who do use it have good results; for ex-
ample, American Express reports a 10- to 15-
percent increase in credit-card use. Another
notable marketing application is market-bas-
ket analysis (Agrawal et al. 1996) systems,
which find patterns such as, “If customer
bought X, he/she is also likely to buy Y and
Z.” Such patterns are valuable to retailers.

Investment:

Numerous companies use da-

ta mining for investment, but most do not
describe their systems. One exception is LBS
Capital Management. Its system uses expert
systems, neural nets, and genetic algorithms
to manage portfolios totaling $600 million;
since its start in 1993, the system has outper-
formed the broad stock market (Hall, Mani,
and Barr 1996).

Fraud detection:

HNC Falcon and Nestor

PRISM

systems are used for monitoring credit-

card fraud, watching over millions of ac-
counts. The

FAIS

system (Senator et al. 1995),

from the U.S. Treasury Financial Crimes En-
forcement Network, is used to identify finan-
cial transactions that might indicate money-
laundering activity.

Manufacturing:

The

C A S S I O P E E

trou-

bleshooting system, developed as part of a
joint venture between General Electric and
SNECMA, was applied by three major Euro-
pean airlines to diagnose and predict prob-
lems for the Boeing 737. To derive families of
faults, clustering methods are used.

CASSIOPEE

received the European first prize for innova-

intimately familiar with the data and serving
as an interface between the data and the users
and products.

For these (and many other) applications,

this form of manual probing of a data set is
slow, expensive, and highly subjective. In
fact, as data volumes grow dramatically, this
type of manual data analysis is becoming
completely impractical in many domains.
Databases are increasing in size in two ways:
(1) the number N of records or objects in the
database and (2) the number d of fields or at-
tributes to an object. Databases containing on
the order of N = 10

objects are becoming in-

creasingly common, for example, in the as-
tronomical sciences. Similarly, the number of
fields d can easily be on the order of 10

even 10

, for example, in medical diagnostic

applications. Who could be expected to di-
gest millions of records, each having tens or
hundreds of fields? We believe that this job is
certainly not one for humans; hence, analysis
work needs to be automated, at least partially.

The need to scale up human analysis capa-

bilities to handling the large number of bytes
that we can collect is both economic and sci-
entific. Businesses use data to gain competi-
tive advantage, increase efficiency, and pro-
vide more valuable services to customers.
Data we capture about our environment are
the basic evidence we use to build theories
and models of the universe we live in. Be-
cause computers have enabled humans to
gather more data than we can digest, it is on-
ly natural to turn to computational tech-
niques to help us unearth meaningful pat-
terns and structures from the massive
volumes of data. Hence, KDD is an attempt to
address a problem that the digital informa-
tion era made a fact of life for all of us: data
overload.

Data Mining and Knowledge

Discovery in the Real World

A large degree of the current interest in KDD
is the result of the media interest surrounding
successful KDD applications, for example, the
focus articles within the last two years in
Business Week, Newsweek, Byte, PC Week, and
other large-circulation periodicals. Unfortu-
nately, it is not always easy to separate fact
from media hype. Nonetheless, several well-
documented examples of successful systems
can rightly be referred to as KDD applications
and have been deployed in operational use
on large-scale real-world problems in science
and in business.

In science, one of the primary application

There is an

urgent need

for a new

generation of

computation-

al theories

and tools to

assist

humans in

extracting

useful

information

(knowledge)

from the

rapidly

growing

volumes of

digital

data.

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AI MAGAZINE

tive applications (Manago and Auriol 1996).

Telecommunications:

The telecommuni-

cations alarm-sequence analyzer (

TASA

) was

built in cooperation with a manufacturer of
telecommunications equipment and three
telephone networks (Mannila, Toivonen, and
Verkamo 1995). The system uses a novel
framework for locating frequently occurring
alarm episodes from the alarm stream and
presenting them as rules. Large sets of discov-
ered rules can be explored with flexible infor-
mation-retrieval tools supporting interactivity
and iteration. In this way,

TASA

offers pruning,

grouping, and ordering tools to refine the re-
sults of a basic brute-force search for rules.

Data cleaning:

The

MERGE

PURGE

system

was applied to the identification of duplicate
welfare claims (Hernandez and Stolfo 1995).
It was used successfully on data from the Wel-
fare Department of the State of Washington.

In other areas, a well-publicized system is

IBM’s

ADVANCED SCOUT

, a specialized data-min-

ing system that helps National Basketball As-
sociation (NBA) coaches organize and inter-
pret data from NBA games (U.S. News 1995).

ADVANCED SCOUT

was used by several of the

NBA teams in 1996, including the Seattle Su-
personics, which reached the NBA finals.

Finally, a novel and increasingly important

type of discovery is one based on the use of in-
telligent agents to navigate through an infor-
mation-rich environment. Although the idea
of active triggers has long been analyzed in the
database field, really successful applications of
this idea appeared only with the advent of the
Internet. These systems ask the user to specify
a profile of interest and search for related in-
formation among a wide variety of public-do-
main and proprietary sources. For example,

FIREFLY

is a personal music-recommendation

agent: It asks a user his/her opinion of several
music pieces and then suggests other music
that the user might like (<http://
www.ffly.com/>).

CRAYON

(http://crayon.net/>)

allows users to create their own free newspaper
(supported by ads);

NEWSHOUND

(<http://www.

sjmercury.com/hound/>) from the San Jose
Mercury News and

FARCAST

(<http://www.far-

cast.com/> automatically search information
from a wide variety of sources, including
newspapers and wire services, and e-mail rele-
vant documents directly to the user.

These are just a few of the numerous such

systems that use KDD techniques to automat-
ically produce useful information from large
masses of raw data. See Piatetsky-Shapiro et
al. (1996) for an overview of issues in devel-
oping industrial KDD applications.

Data Mining and KDD

Historically, the notion of finding useful pat-
terns in data has been given a variety of
names, including data mining, knowledge ex-
traction, information discovery, information
harvesting, data archaeology, and data pattern
processing. The term data mining has mostly
been used by statisticians, data analysts, and
the management information systems (MIS)
communities. It has also gained popularity in
the database field. The phrase knowledge dis-
covery in databases was coined at the first KDD
workshop in 1989 (Piatetsky-Shapiro 1991) to
emphasize that knowledge is the end product
of a data-driven discovery. It has been popular-
ized in the AI and machine-learning fields.

In our view, KDD refers to the overall pro-

cess of discovering useful knowledge from da-
ta, and data mining refers to a particular step
in this process. Data mining is the application
of specific algorithms for extracting patterns
from data. The distinction between the KDD
process and the data-mining step (within the
process) is a central point of this article. The
additional steps in the KDD process, such as
data preparation, data selection, data cleaning,
incorporation of appropriate prior knowledge,
and proper interpretation of the results of
mining, are essential to ensure that useful
knowledge is derived from the data. Blind ap-
plication of data-mining methods (rightly crit-
icized as data dredging in the statistical litera-
ture) can be a dangerous activity, easily
leading to the discovery of meaningless and
invalid patterns.

The Interdisciplinary Nature of KDD

KDD has evolved, and continues to evolve,
from the intersection of research fields such as
machine learning, pattern recognition,
databases, statistics, AI, knowledge acquisition
for expert systems, data visualization, and
high-performance computing. The unifying
goal is extracting high-level knowledge from
low-level data in the context of large data sets.

The data-mining component of KDD cur-

rently relies heavily on known techniques
from machine learning, pattern recognition,
and statistics to find patterns from data in the
data-mining step of the KDD process. A natu-
ral question is, How is KDD different from pat-
tern recognition or machine learning (and re-
lated fields)? The answer is that these fields
provide some of the data-mining methods
that are used in the data-mining step of the
KDD process. KDD focuses on the overall pro-
cess of knowledge discovery from data, includ-
ing how the data are stored and accessed, how
algorithms can be scaled to massive data sets

The basic
problem
addressed by
the KDD
process is
one of
mapping
low-level
data into
other forms
that might be
more
compact,
more
abstract,
or more
useful.

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FALL 1996 39

A driving force behind KDD is the database

field (the second D in KDD). Indeed, the
problem of effective data manipulation when
data cannot fit in the main memory is of fun-
damental importance to KDD. Database tech-
niques for gaining efficient data access,
grouping and ordering operations when ac-
cessing data, and optimizing queries consti-
tute the basics for scaling algorithms to larger
data sets. Most data-mining algorithms from
statistics, pattern recognition, and machine
learning assume data are in the main memo-
ry and pay no attention to how the algorithm
breaks down if only limited views of the data
are possible.

A related field evolving from databases is

data warehousing, which refers to the popular
business trend of collecting and cleaning
transactional data to make them available for
online analysis and decision support. Data
warehousing helps set the stage for KDD in
two important ways: (1) data cleaning and (2)
data access.

Data cleaning:

As organizations are forced

to think about a unified logical view of the
wide variety of data and databases they pos-
sess, they have to address the issues of map-
ping data to a single naming convention,
uniformly representing and handling missing
data, and handling noise and errors when
possible.

Data access:

Uniform and well-defined

methods must be created for accessing the da-
ta and providing access paths to data that
were historically difficult to get to (for exam-
ple, stored offline).

Once organizations and individuals have

solved the problem of how to store and ac-
cess their data, the natural next step is the
question, What else do we do with all the da-
ta? This is where opportunities for KDD natu-
rally arise.

A popular approach for analysis of data

warehouses is called online analytical processing
(OLAP), named for a set of principles pro-
posed by Codd (1993). OLAP tools focus on
providing multidimensional data analysis,
which is superior to

SQL

in computing sum-

maries and breakdowns along many dimen-
sions. OLAP tools are targeted toward simpli-
fying and supporting interactive data analysis,
but the goal of KDD tools is to automate as
much of the process as possible. Thus, KDD is
a step beyond what is currently supported by
most standard database systems.

Basic Definitions

KDD is the nontrivial process of identifying
valid, novel, potentially useful, and ultimate-

and still run efficiently, how results can be in-
terpreted and visualized, and how the overall
man-machine interaction can usefully be
modeled and supported. The KDD process
can be viewed as a multidisciplinary activity
that encompasses techniques beyond the
scope of any one particular discipline such as
machine learning. In this context, there are
clear opportunities for other fields of AI (be-
sides machine learning) to contribute to
KDD. KDD places a special emphasis on find-
ing understandable patterns that can be inter-
preted as useful or interesting knowledge.
Thus, for example, neural networks, although
a powerful modeling tool, are relatively
difficult to understand compared to decision
trees. KDD also emphasizes scaling and ro-
bustness properties of modeling algorithms
for large noisy data sets.

Related AI research fields include machine

discovery, which targets the discovery of em-
pirical laws from observation and experimen-
tation (Shrager and Langley 1990) (see Kloes-
gen and Zytkow [1996] for a glossary of terms
common to KDD and machine discovery),
and causal modeling for the inference of
causal models from data (Spirtes, Glymour,
and Scheines 1993). Statistics in particular
has much in common with KDD (see Elder
and Pregibon [1996] and Glymour et al.
[1996] for a more detailed discussion of this
synergy). Knowledge discovery from data is
fundamentally a statistical endeavor. Statistics
provides a language and framework for quan-
tifying the uncertainty that results when one
tries to infer general patterns from a particu-
lar sample of an overall population. As men-
tioned earlier, the term data mining has had
negative connotations in statistics since the
1960s when computer-based data analysis
techniques were first introduced. The concern
arose because if one searches long enough in
any data set (even randomly generated data),
one can find patterns that appear to be statis-
tically significant but, in fact, are not. Clearly,
this issue is of fundamental importance to
KDD. Substantial progress has been made in
recent years in understanding such issues in
statistics. Much of this work is of direct rele-
vance to KDD. Thus, data mining is a legiti-
mate activity as long as one understands how
to do it correctly; data mining carried out
poorly (without regard to the statistical as-
pects of the problem) is to be avoided. KDD
can also be viewed as encompassing a broader
view of modeling than statistics. KDD aims to
provide tools to automate (to the degree pos-
sible) the entire process of data analysis and
the statistician’s “art” of hypothesis selection.

Data mining

is a step in

the KDD

process that

consists of ap-

plying data

analysis and

discovery al-

gorithms that

produce a par-

ticular enu-

meration of

patterns

(or models)

over the

data.

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AI MAGAZINE

ly understandable patterns in data (Fayyad,
Piatetsky-Shapiro, and Smyth 1996).

Here, data are a set of facts (for example,

cases in a database), and pattern is an expres-
sion in some language describing a subset of
the data or a model applicable to the subset.
Hence, in our usage here, extracting a pattern
also designates fitting a model to data; find-
ing structure from data; or, in general, mak-
ing any high-level description of a set of data.
The term process implies that KDD comprises
many steps, which involve data preparation,
search for patterns, knowledge evaluation,
and refinement, all repeated in multiple itera-
tions. By nontrivial, we mean that some
search or inference is involved; that is, it is
not a straightforward computation of
predefined quantities like computing the av-
erage value of a set of numbers.

The discovered patterns should be valid on

new data with some degree of certainty. We
also want patterns to be novel (at least to the
system and preferably to the user) and poten-
tially useful, that is, lead to some benefit to
the user or task. Finally, the patterns should
be understandable, if not immediately then
after some postprocessing.

The previous discussion implies that we can

define quantitative measures for evaluating
extracted patterns. In many cases, it is possi-
ble to define measures of certainty (for exam-
ple, estimated prediction accuracy on new

data) or utility (for example, gain, perhaps in
dollars saved because of better predictions or
speedup in response time of a system). No-
tions such as novelty and understandability
are much more subjective. In certain contexts,
understandability can be estimated by sim-
plicity (for example, the number of bits to de-
scribe a pattern). An important notion, called
interestingness (for example, see Silberschatz
and Tuzhilin [1995] and Piatetsky-Shapiro and
Matheus [1994]), is usually taken as an overall
measure of pattern value, combining validity,
novelty, usefulness, and simplicity. Interest-
ingness functions can be defined explicitly or
can be manifested implicitly through an or-
dering placed by the KDD system on the dis-
covered patterns or models.

Given these notions, we can consider a

pattern to be knowledge if it exceeds some in-
terestingness threshold, which is by no
means an attempt to define knowledge in the
philosophical or even the popular view. As a
matter of fact, knowledge in this definition is
purely user oriented and domain specific and
is determined by whatever functions and
thresholds the user chooses.

Data mining is a step in the KDD process

that consists of applying data analysis and
discovery algorithms that, under acceptable
computational efficiency limitations, pro-
duce a particular enumeration of patterns (or
models) over the data. Note that the space of

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FALL 1996 41

Data

Transformed

Data

Patterns

Preprocessing

Data Mining

Interpretation /

Evaluation

Transformation

Selection

--- --- ---

Knowledge

Preprocessed Data

Target Date

Figure 1. An Overview of the Steps That Compose the KDD Process.

methods, the effective number of variables
under consideration can be reduced, or in-
variant representations for the data can be
found.

Fifth is matching the goals of the KDD pro-

cess (step 1) to a particular data-mining
method. For example, summarization, clas-
sification, regression, clustering, and so on,
are described later as well as in Fayyad, Piatet-
sky-Shapiro, and Smyth (1996).

Sixth is exploratory analysis and model

and hypothesis selection: choosing the data-
mining algorithm(s) and selecting method(s)
to be used for searching for data patterns.
This process includes deciding which models
and parameters might be appropriate (for ex-
ample, models of categorical data are differ-
ent than models of vectors over the reals) and
matching a particular data-mining method
with the overall criteria of the KDD process
(for example, the end user might be more in-
terested in understanding the model than its
predictive capabilities).

Seventh is data mining: searching for pat-

terns of interest in a particular representa-
tional form or a set of such representations,
including classification rules or trees, regres-
sion, and clustering. The user can significant-
ly aid the data-mining method by correctly
performing the preceding steps.

Eighth is interpreting mined patterns, pos-

sibly returning to any of steps 1 through 7 for
further iteration. This step can also involve
visualization of the extracted patterns and
models or visualization of the data given the
extracted models.

Ninth is acting on the discovered knowl-

edge: using the knowledge directly, incorpo-
rating the knowledge into another system for
further action, or simply documenting it and
reporting it to interested parties. This process
also includes checking for and resolving po-
tential conflicts with previously believed (or
extracted) knowledge.

The KDD process can involve significant

iteration and can contain loops between
any two steps. The basic flow of steps (al-
though not the potential multitude of itera-
tions and loops) is illustrated in figure 1.
Most previous work on KDD has focused on
step 7, the data mining. However, the other
steps are as important (and probably more
so) for the successful application of KDD in
practice. Having defined the basic notions
and introduced the KDD process, we now
focus on the data-mining component,
which has, by far, received the most atten-
tion in the literature.

patterns is often infinite, and the enumera-
tion of patterns involves some form of
search in this space. Practical computational
constraints place severe limits on the sub-
space that can be explored by a data-mining
algorithm.

The KDD process involves using the

database along with any required selection,
preprocessing, subsampling, and transforma-
tions of it; applying data-mining methods
(algorithms) to enumerate patterns from it;
and evaluating the products of data mining
to identify the subset of the enumerated pat-
terns deemed knowledge. The data-mining
component of the KDD process is concerned
with the algorithmic means by which pat-
terns are extracted and enumerated from da-
ta. The overall KDD process (figure 1) in-
cludes the evaluation and possible
interpretation of the mined patterns to de-
termine which patterns can be considered
new knowledge. The KDD process also in-
cludes all the additional steps described in
the next section.

The notion of an overall user-driven pro-

cess is not unique to KDD: analogous propos-
als have been put forward both in statistics
(Hand 1994) and in machine learning (Brod-
ley and Smyth 1996).

The KDD Process

The KDD process is interactive and iterative,
involving numerous steps with many deci-
sions made by the user. Brachman and Anand
(1996) give a practical view of the KDD pro-
cess, emphasizing the interactive nature of
the process. Here, we broadly outline some of
its basic steps:

First is developing an understanding of the

application domain and the relevant prior
knowledge and identifying the goal of the
KDD process from the customer’s viewpoint.

Second is creating a target data set: select-

ing a data set, or focusing on a subset of vari-
ables or data samples, on which discovery is
to be performed.

Third is data cleaning and preprocessing.

Basic operations include removing noise if
appropriate, collecting the necessary informa-
tion to model or account for noise, deciding
on strategies for handling missing data fields,
and accounting for time-sequence informa-
tion and known changes.

Fourth is data reduction and projection:

finding useful features to represent the data
depending on the goal of the task. With di-
mensionality reduction or transformation

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The Data-Mining Step

of the KDD Process

The data-mining component of the KDD pro-
cess often involves repeated iterative applica-
tion of particular data-mining methods. This
section presents an overview of the primary
goals of data mining, a description of the
methods used to address these goals, and a
brief description of the data-mining algo-
rithms that incorporate these methods.

The knowledge discovery goals are defined

by the intended use of the system. We can
distinguish two types of goals: (1) verification
and (2) discovery. With verification, the sys-
tem is limited to verifying the user’s hypothe-
sis. With discovery, the system autonomously
finds new patterns. We further subdivide the
discovery goal into prediction, where the sys-
tem finds patterns for predicting the future
behavior of some entities, and description,
where the system finds patterns for presenta-
tion to a user in a human-understandable
form. In this article, we are primarily con-
cerned with discovery-oriented data mining.

Data mining involves fitting models to, or

determining patterns from, observed data.
The fitted models play the role of inferred
knowledge: Whether the models reflect useful
or interesting knowledge is part of the over-
all, interactive KDD process where subjective
human judgment is typically required. Two
primary mathematical formalisms are used in
model fitting: (1) statistical and (2) logical.
The statistical approach allows for nondeter-
ministic effects in the model, whereas a logi-
cal model is purely deterministic. We focus
primarily on the statistical approach to data
mining, which tends to be the most widely
used basis for practical data-mining applica-
tions given the typical presence of uncertain-
ty in real-world data-generating processes.

Most data-mining methods are based on

tried and tested techniques from machine
learning, pattern recognition, and statistics:
classification, clustering, regression, and so
on. The array of different algorithms under
each of these headings can often be bewilder-
ing to both the novice and the experienced
data analyst. It should be emphasized that of
the many data-mining methods advertised in
the literature, there are really only a few fun-
damental techniques. The actual underlying
model representation being used by a particu-
lar method typically comes from a composi-
tion of a small number of well-known op-
tions: polynomials, splines, kernel and basis
functions, threshold-Boolean functions, and
so on. Thus, algorithms tend to differ primar-

ily in the goodness-of-fit criterion used to
evaluate model fit or in the search method
used to find a good fit.

In our brief overview of data-mining meth-

ods, we try in particular to convey the notion
that most (if not all) methods can be viewed
as extensions or hybrids of a few basic tech-
niques and principles. We first discuss the pri-
mary methods of data mining and then show
that the data- mining methods can be viewed
as consisting of three primary algorithmic
components: (1) model representation, (2)
model evaluation, and (3) search. In the dis-
cussion of KDD and data-mining methods,
we use a simple example to make some of the
notions more concrete. Figure 2 shows a sim-
ple two-dimensional artificial data set consist-
ing of 23 cases. Each point on the graph rep-
resents a person who has been given a loan
by a particular bank at some time in the past.
The horizontal axis represents the income of
the person; the vertical axis represents the to-
tal personal debt of the person (mortgage, car
payments, and so on). The data have been
classified into two classes: (1) the x’s repre-
sent persons who have defaulted on their
loans and (2) the o’s represent persons whose
loans are in good status with the bank. Thus,
this simple artificial data set could represent a
historical data set that can contain useful
knowledge from the point of view of the
bank making the loans. Note that in actual
KDD applications, there are typically many
more dimensions (as many as several hun-
dreds) and many more data points (many
thousands or even millions).

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FALL 1996 43

Income

Debt

Figure 2. A Simple Data Set with Two Classes Used for Illustrative Purposes.

The purpose here is to illustrate basic ideas

on a small problem in two-dimensional
space.

Data-Mining Methods

The two high-level primary goals of data min-
ing in practice tend to be prediction and de-
scription. As stated earlier, prediction in-
volves using some variables or fields in the
database to predict unknown or future values
of other variables of interest, and description
focuses on finding human-interpretable pat-
terns describing the data. Although the
boundaries between prediction and descrip-
tion are not sharp (some of the predictive
models can be descriptive, to the degree that
they are understandable, and vice versa), the
distinction is useful for understanding the
overall discovery goal. The relative impor-
tance of prediction and description for partic-
ular data-mining applications can vary con-
siderably. The goals of prediction and
description can be achieved using a variety of
particular data-mining methods.

Classification is learning a function that

maps (classifies) a data item into one of sever-
al predefined classes (Weiss and Kulikowski
1991; Hand 1981). Examples of classification
methods used as part of knowledge discovery
applications include the classifying of trends
in financial markets (Apte and Hong 1996)
and the automated identification of objects of
interest in large image databases (Fayyad,
Djorgovski, and Weir 1996). Figure 3 shows a
simple partitioning of the loan data into two
class regions; note that it is not possible to
separate the classes perfectly using a linear
decision boundary. The bank might want to
use the classification regions to automatically
decide whether future loan applicants will be
given a loan or not.

Regression is learning a function that maps

a data item to a real-valued prediction vari-
able. Regression applications are many, for
example, predicting the amount of biomass
present in a forest given remotely sensed mi-
crowave measurements, estimating the proba-
bility that a patient will survive given the re-
sults of a set of diagnostic tests, predicting
consumer demand for a new product as a
function of advertising expenditure, and pre-
dicting time series where the input variables
can be time-lagged versions of the prediction
variable. Figure 4 shows the result of simple
linear regression where total debt is fitted as a
linear function of income: The fit is poor be-
cause only a weak correlation exists between
the two variables.

Clustering is a common descriptive task

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Figure 3. A Simple Linear Classification Boundary for the Loan Data Set.

The shaped region denotes class no loan.

Income

Debt

No Loan

Loan

Figure 4. A Simple Linear Regression for the Loan Data Set.

Income

Debt

Regression Line

where one seeks to identify a finite set of cat-
egories or clusters to describe the data (Jain
and Dubes 1988; Titterington, Smith, and
Makov 1985). The categories can be mutually
exclusive and exhaustive or consist of a richer
representation, such as hierarchical or over-
lapping categories. Examples of clustering ap-
plications in a knowledge discovery context
include discovering homogeneous subpopula-
tions for consumers in marketing databases
and identifying subcategories of spectra from
infrared sky measurements (Cheeseman and
Stutz 1996). Figure 5 shows a possible cluster-
ing of the loan data set into three clusters;
note that the clusters overlap, allowing data
points to belong to more than one cluster.
The original class labels (denoted by x’s and
o’s in the previous figures) have been replaced
by a + to indicate that the class membership
is no longer assumed known. Closely related
to clustering is the task of probability density
estimation, which consists of techniques for
estimating from data the joint multivariate
probability density function of all the vari-
ables or fields in the database (Silverman
1986).

Summarization involves methods for find-

ing a compact description for a subset of da-
ta. A simple example would be tabulating the
mean and standard deviations for all fields.
More sophisticated methods involve the
derivation of summary rules (Agrawal et al.
1996), multivariate visualization techniques,
and the discovery of functional relationships
between variables (Zembowicz and Zytkow
1996). Summarization techniques are often
applied to interactive exploratory data analy-
sis and automated report generation.

Dependency modeling consists of finding a

model that describes significant dependencies
between variables. Dependency models exist
at two levels: (1) the structural level of the
model specifies (often in graphic form) which
variables are locally dependent on each other
and (2) the quantitative level of the model
specifies the strengths of the dependencies
using some numeric scale. For example, prob-
abilistic dependency networks use condition-
al independence to specify the structural as-
pect of the model and probabilities or
correlations to specify the strengths of the de-
pendencies (Glymour et al. 1987; Heckerman
1996). Probabilistic dependency networks are
increasingly finding applications in areas as
diverse as the development of probabilistic
medical expert systems from databases, infor-
mation retrieval, and modeling of the human
genome.

Change and deviation detection focuses on

discovering the most significant changes in
the data from previously measured or norma-
tive values (Berndt and Clifford 1996; Guyon,
Matic, and Vapnik 1996; Kloesgen 1996;
Matheus, Piatetsky-Shapiro, and McNeill
1996; Basseville and Nikiforov 1993).

The Components of
Data-Mining Algorithms

The next step is to construct specific algo-
rithms to implement the general methods we
outlined. One can identify three primary
components in any data-mining algorithm:
(1) model representation, (2) model evalua-
tion, and (3) search.

This reductionist view is not necessarily

complete or fully encompassing; rather, it is a
convenient way to express the key concepts
of data-mining algorithms in a relatively
unified and compact manner. Cheeseman
(1990) outlines a similar structure.

Model representation is the language used to

describe discoverable patterns. If the repre-
sentation is too limited, then no amount of
training time or examples can produce an ac-
curate model for the data. It is important that
a data analyst fully comprehend the represen-
tational assumptions that might be inherent
in a particular method. It is equally impor-
tant that an algorithm designer clearly state
which representational assumptions are being
made by a particular algorithm. Note that in-
creased representational power for models in-
creases the danger of overfitting the training
data, resulting in reduced prediction accuracy
on unseen data.

Model-evaluation criteria are quantitative

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FALL 1996 45

Income

Debt

Cluster 2

Cluster 3

Cluster 1

Figure 5. A Simple Clustering of the Loan Data Set into Three Clusters.

Note that original labels are replaced by a +.

Decision Trees and Rules

Decision trees and rules that use univariate
splits have a simple representational form,
making the inferred model relatively easy for
the user to comprehend. However, the restric-
tion to a particular tree or rule representation
can significantly restrict the functional form
(and, thus, the approximation power) of the
model. For example, figure 6 illustrates the ef-
fect of a threshold split applied to the income
variable for a loan data set: It is clear that us-
ing such simple threshold splits (parallel to
the feature axes) severely limits the type of
classification boundaries that can be induced.
If one enlarges the model space to allow more
general expressions (such as multivariate hy-
perplanes at arbitrary angles), then the model
is more powerful for prediction but can be
much more difficult to comprehend. A large
number of decision tree and rule-induction
algorithms are described in the machine-
learning and applied statistics literature
(Quinlan 1992; Breiman et al. 1984).

To a large extent, they depend on likeli-

hood-based model-evaluation methods, with
varying degrees of sophistication in terms of
penalizing model complexity. Greedy search
methods, which involve growing and prun-
ing rule and tree structures, are typically used
to explore the superexponential space of pos-
sible models. Trees and rules are primarily
used for predictive modeling, both for clas-
sification (Apte and Hong 1996; Fayyad, Djor-
govski, and Weir 1996) and regression, al-
though they can also be applied to summary
descriptive modeling (Agrawal et al. 1996).

Nonlinear Regression and
Classification Methods

These methods consist of a family of tech-
niques for prediction that fit linear and non-
linear combinations of basis functions (sig-
moids, splines, polynomials) to combinations
of the input variables. Examples include feed-
forward neural networks, adaptive spline
methods, and projection pursuit regression
(see Elder and Pregibon [1996], Cheng and
Titterington [1994], and Friedman [1989] for
more detailed discussions). Consider neural
networks, for example. Figure 7 illustrates the
type of nonlinear decision boundary that a
neural network might find for the loan data
set. In terms of model evaluation, although
networks of the appropriate size can univer-
sally approximate any smooth function to
any desired degree of accuracy, relatively little
is known about the representation properties
of fixed-size networks estimated from finite
data sets. Also, the standard squared error and

statements (or fit functions) of how well a par-
ticular pattern (a model and its parameters)
meets the goals of the KDD process. For ex-
ample, predictive models are often judged by
the empirical prediction accuracy on some
test set. Descriptive models can be evaluated
along the dimensions of predictive accuracy,
novelty, utility, and understandability of the
fitted model.

Search method consists of two components:

(1) parameter search and (2) model search.
Once the model representation (or family of
representations) and the model-evaluation
criteria are fixed, then the data-mining prob-
lem has been reduced to purely an optimiza-
tion task: Find the parameters and models
from the selected family that optimize the
evaluation criteria. In parameter search, the
algorithm must search for the parameters
that optimize the model-evaluation criteria
given observed data and a fixed model repre-
sentation. Model search occurs as a loop over
the parameter-search method: The model rep-
resentation is changed so that a family of
models is considered.

Some Data-Mining Methods

A wide variety of data-mining methods exist,
but here, we only focus on a subset of popu-
lar techniques. Each method is discussed in
the context of model representation, model
evaluation, and search.

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Income

Debt

No Loan

Loan

Figure 6. Using a Single Threshold on the Income Variable to

Try to Classify the Loan Data Set.

cross-entropy loss functions used to train
neural networks can be viewed as log-likeli-
hood functions for regression and
classification, respectively (Ripley 1994; Ge-
man, Bienenstock, and Doursat 1992). Back
propagation is a parameter-search method
that performs gradient descent in parameter
(weight) space to find a local maximum of
the likelihood function starting from random
initial conditions. Nonlinear regression meth-
ods, although powerful in representational
power, can be difficult to interpret.

For example, although the classification

boundaries of figure 7 might be more accu-
rate than the simple threshold boundary of
figure 6, the threshold boundary has the ad-
vantage that the model can be expressed, to
some degree of certainty, as a simple rule of
the form “if income is greater than threshold,
then loan will have good status.”

Example-Based Methods

The representation is simple: Use representa-
tive examples from the database to approxi-
mate a model; that is, predictions on new ex-
amples are derived from the properties of
similar examples in the model whose predic-
tion is known. Techniques include nearest-
neighbor classification and regression algo-
rithms (Dasarathy 1991) and case-based
reasoning systems (Kolodner 1993). Figure 8
illustrates the use of a nearest-neighbor clas-
sifier for the loan data set: The class at any
new point in the two-dimensional space is
the same as the class of the closest point in
the original training data set.

A potential disadvantage of example-based

methods (compared with tree-based methods)
is that a well-defined distance metric for eval-
uating the distance between data points is re-
quired. For the loan data in figure 8, this
would not be a problem because income and
debt are measured in the same units. Howev-
er, if one wished to include variables such as
the duration of the loan, sex, and profession,
then it would require more effort to define a
sensible metric between the variables. Model
evaluation is typically based on cross-valida-
tion estimates (Weiss and Kulikowski 1991) of
a prediction error: Parameters of the model to
be estimated can include the number of
neighbors to use for prediction and the dis-
tance metric itself. Like nonlinear regression
methods, example-based methods are often
asymptotically powerful in terms of approxi-
mation properties but, conversely, can be
difficult to interpret because the model is im-
plicit in the data and not explicitly formulat-
ed. Related techniques include kernel-density

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FALL 1996 47

Income

Debt

No Loan

Loan

Figure 7. An Example of Classification Boundaries Learned by a Nonlinear

Classifier (Such as a Neural Network) for the Loan Data Set.

Income

Debt

No Loan

Loan

Figure 8. Classification Boundaries for a Nearest-Neighbor

Classifier for the Loan Data Set.

evitably limited in scope; many data-mining
techniques, particularly specialized methods
for particular types of data and domains, were
not mentioned specifically. We believe the
general discussion on data-mining tasks and
components has general relevance to a vari-
ety of methods. For example, consider time-
series prediction, which traditionally has
been cast as a predictive regression task (au-
toregressive models, and so on). Recently,
more general models have been developed for
time-series applications, such as nonlinear ba-
sis functions, example-based models, and ker-
nel methods. Furthermore, there has been
significant interest in descriptive graphic and
local data modeling of time series rather than
purely predictive modeling (Weigend and
Gershenfeld 1993). Thus, although different
algorithms and applications might appear dif-
ferent on the surface, it is not uncommon to
find that they share many common compo-
nents. Understanding data mining and model
induction at this component level clarifies
the behavior of any data-mining algorithm
and makes it easier for the user to understand
its overall contribution and applicability to
the KDD process.

An important point is that each technique

typically suits some problems better than
others. For example, decision tree classifiers
can be useful for finding structure in high-di-
mensional spaces and in problems with
mixed continuous and categorical data (be-
cause tree methods do not require distance
metrics). However, classification trees might
not be suitable for problems where the true
decision boundaries between classes are de-
scribed by a second-order polynomial (for ex-
ample). Thus, there is no universal data-min-
ing method, and choosing a particular
algorithm for a particular application is some-
thing of an art. In practice, a large portion of
the application effort can go into properly
formulating the problem (asking the right
question) rather than into optimizing the al-
gorithmic details of a particular data-mining
method (Langley and Simon 1995; Hand
1994).

Because our discussion and overview of da-

ta-mining methods has been brief, we want
to make two important points clear:

First, our overview of automated search fo-

cused mainly on automated methods for ex-
tracting patterns or models from data. Al-
though this approach is consistent with the
definition we gave earlier, it does not neces-
sarily represent what other communities
might refer to as data mining. For example,
some use the term to designate any manual

estimation (Silverman 1986) and mixture
modeling (Titterington, Smith, and Makov
1985).

Probabilistic Graphic
Dependency Models

Graphic models specify probabilistic depen-
dencies using a graph structure (Whittaker
1990; Pearl 1988). In its simplest form, the
model specifies which variables are directly de-
pendent on each other. Typically, these mod-
els are used with categorical or discrete-valued
variables, but extensions to special cases, such
as Gaussian densities, for real-valued variables
are also possible. Within the AI and statistical
communities, these models were initially de-
veloped within the framework of probabilistic
expert systems; the structure of the model and
the parameters (the conditional probabilities
attached to the links of the graph) were elicit-
ed from experts. Recently, there has been sig-
nificant work in both the AI and statistical
communities on methods whereby both the
structure and the parameters of graphic mod-
els can be learned directly from databases
(Buntine 1996; Heckerman 1996). Model-eval-
uation criteria are typically Bayesian in form,
and parameter estimation can be a mixture of
closed-form estimates and iterative methods
depending on whether a variable is directly
observed or hidden. Model search can consist
of greedy hill-climbing methods over various
graph structures. Prior knowledge, such as a
partial ordering of the variables based on
causal relations, can be useful in terms of re-
ducing the model search space. Although still
primarily in the research phase, graphic model
induction methods are of particular interest to
KDD because the graphic form of the model
lends itself easily to human interpretation.

Relational Learning Models

Although decision trees and rules have a repre-
sentation restricted to propositional logic, rela-
tional learning (also known as inductive logic
programming) uses the more flexible pattern
language of first-order logic. A relational learn-
er can easily find formulas such as X = Y. Most
research to date on model-evaluation methods
for relational learning is logical in nature. The
extra representational power of relational
models comes at the price of significant com-
putational demands in terms of search. See
Dzeroski (1996) for a more detailed discussion.

Discussion

Given the broad spectrum of data-mining
methods and algorithms, our overview is in-

Understand-

ing data

mining and

model

induction at

this

component

level clarifies

the behavior

of any

data-mining

algorithm

and makes it

easier for the

user to

understand its

overall

contribution

and

applicability

to the

KDD

process.

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search of the data or search assisted by queries
to a database management system or to refer
to humans visualizing patterns in data. In
other communities, it is used to refer to the
automated correlation of data from transac-
tions or the automated generation of transac-
tion reports. We choose to focus only on
methods that contain certain degrees of
search autonomy.

Second, beware the hype: The state of the

art in automated methods in data mining is
still in a fairly early stage of development.
There are no established criteria for deciding
which methods to use in which circum-
stances, and many of the approaches are
based on crude heuristic approximations to
avoid the expensive search required to find
optimal, or even good, solutions. Hence, the
reader should be careful when confronted
with overstated claims about the great ability
of a system to mine useful information from
large (or even small) databases.

Application Issues

For a survey of KDD applications as well as
detailed examples, see Piatetsky-Shapiro et al.
(1996) for industrial applications and Fayyad,
Haussler, and Stolorz (1996) for applications
in science data analysis. Here, we examine
criteria for selecting potential applications,
which can be divided into practical and tech-
nical categories. The practical criteria for KDD
projects are similar to those for other applica-
tions of advanced technology and include the
potential impact of an application, the ab-
sence of simpler alternative solutions, and
strong organizational support for using tech-
nology. For applications dealing with person-
al data, one should also consider the privacy
and legal issues (Piatetsky-Shapiro 1995).

The technical criteria include considera-

tions such as the availability of sufficient data
(cases). In general, the more fields there are
and the more complex the patterns being
sought, the more data are needed. However,
strong prior knowledge (see discussion later)
can reduce the number of needed cases sig-
nificantly. Another consideration is the rele-
vance of attributes. It is important to have da-
ta attributes that are relevant to the discovery
task; no amount of data will allow prediction
based on attributes that do not capture the
required information. Furthermore, low noise
levels (few data errors) are another considera-
tion. High amounts of noise make it hard to
identify patterns unless a large number of cas-
es can mitigate random noise and help clarify
the aggregate patterns. Changing and time-

oriented data, although making the applica-
tion development more difficult, make it po-
tentially much more useful because it is easier
to retrain a system than a human. Finally,
and perhaps one of the most important con-
siderations, is prior knowledge. It is useful to
know something about the domain —what
are the important fields, what are the likely
relationships, what is the user utility func-
tion, what patterns are already known, and so
on.

Research and Application Challenges

We outline some of the current primary re-
search and application challenges for KDD.
This list is by no means exhaustive and is in-
tended to give the reader a feel for the types
of problem that KDD practitioners wrestle
with.

Larger databases:

Databases with hun-

dreds of fields and tables and millions of
records and of a multigigabyte size are com-
monplace, and terabyte (10

bytes) databases

are beginning to appear. Methods for dealing
with large data volumes include more
efficient algorithms (Agrawal et al. 1996),
sampling, approximation, and massively par-
allel processing (Holsheimer et al. 1996).

High dimensionality:

Not only is there of-

ten a large number of records in the database,
but there can also be a large number of fields
(attributes, variables); so, the dimensionality
of the problem is high. A high-dimensional
data set creates problems in terms of increas-
ing the size of the search space for model in-
duction in a combinatorially explosive man-
ner. In addition, it increases the chances that
a data-mining algorithm will find spurious
patterns that are not valid in general. Ap-
proaches to this problem include methods to
reduce the effective dimensionality of the
problem and the use of prior knowledge to
identify irrelevant variables.

Overfitting:

When the algorithm searches

for the best parameters for one particular
model using a limited set of data, it can mod-
el not only the general patterns in the data
but also any noise specific to the data set, re-
sulting in poor performance of the model on
test data. Possible solutions include cross-vali-
dation, regularization, and other sophisticat-
ed statistical strategies.

Assessing of statistical significance:

problem (related to overfitting) occurs when
the system is searching over many possible
models. For example, if a system tests models
at the 0.001 significance level, then on aver-
age, with purely random data, N/1000 of
these models will be accepted as significant.

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FALL 1996 49

edge is important in all the steps of the KDD
process. Bayesian approaches (for example,
Cheeseman [1990]) use prior probabilities
over data and distributions as one form of en-
coding prior knowledge. Others employ de-
ductive database capabilities to discover
knowledge that is then used to guide the da-
ta-mining search (for example, Simoudis,
Livezey, and Kerber [1995]).

Integration with other systems:

A stand-

alone discovery system might not be very
useful. Typical integration issues include inte-
gration with a database management system
(for example, through a query interface), in-
tegration with spreadsheets and visualization
tools, and accommodating of real-time sensor
readings. Examples of integrated KDD sys-
tems are described by Simoudis, Livezey, and
Kerber (1995) and Stolorz, Nakamura, Mesro-
biam, Muntz, Shek, Santos, Yi, Ng, Chien,
Mechoso, and Farrara (1995).

Concluding Remarks: The

Potential Role of AI in KDD

In addition to machine learning, other AI fiel-
ds can potentially contribute significantly to
various aspects of the KDD process. We men-
tion a few examples of these areas here:

Natural language

presents significant op-

portunities for mining in free-form text, espe-
cially for automated annotation and indexing
prior to classification of text corpora. Limited
parsing capabilities can help substantially in
the task of deciding what an article refers to.
Hence, the spectrum from simple natural lan-
guage processing all the way to language un-
derstanding can help substantially. Also, nat-
ural language processing can contribute
significantly as an effective interface for stat-
ing hints to mining algorithms and visualiz-
ing and explaining knowledge derived by a
KDD system.

Planning

considers a complicated data

analysis process. It involves conducting com-
plicated data-access and data-transformation
operations; applying preprocessing routines;
and, in some cases, paying attention to re-
source and data-access constraints. Typically,
data processing steps are expressed in terms of
desired postconditions and preconditions for
the application of certain routines, which
lends itself easily to representation as a plan-
ning problem. In addition, planning ability
can play an important role in automated
agents (see next item) to collect data samples
or conduct a search to obtain needed data sets.

Intelligent agents

can be fired off to col-

lect necessary information from a variety of

This point is frequently missed by many ini-
tial attempts at KDD. One way to deal with
this problem is to use methods that adjust
the test statistic as a function of the search,
for example, Bonferroni adjustments for inde-
pendent tests or randomization testing.

Changing data and knowledge:

Rapidly

changing (nonstationary) data can make pre-
viously discovered patterns invalid. In addi-
tion, the variables measured in a given appli-
cation database can be modified, deleted, or
augmented with new measurements over
time. Possible solutions include incremental
methods for updating the patterns and treat-
ing change as an opportunity for discovery
by using it to cue the search for patterns of
change only (Matheus, Piatetsky-Shapiro, and
McNeill 1996). See also Agrawal and Psaila
(1995) and Mannila, Toivonen, and Verkamo
(1995).

Missing and noisy data:

This problem is

especially acute in business databases. U.S.
census data reportedly have error rates as
great as 20 percent in some fields. Important
attributes can be missing if the database was
not designed with discovery in mind. Possible
solutions include more sophisticated statisti-
cal strategies to identify hidden variables and
dependencies (Heckerman 1996; Smyth et al.
1996).

Complex relationships between fields:

Hierarchically structured attributes or values,
relations between attributes, and more so-
phisticated means for representing knowl-
edge about the contents of a database will re-
quire algorithms that can effectively use such
information. Historically, data-mining algo-
rithms have been developed for simple at-
tribute-value records, although new tech-
niques for deriving relations between
variables are being developed (Dzeroski 1996;
Djoko, Cook, and Holder 1995).

Understandability of patterns:

In many

applications, it is important to make the dis-
coveries more understandable by humans.
Possible solutions include graphic representa-
tions (Buntine 1996; Heckerman 1996), rule
structuring, natural language generation, and
techniques for visualization of data and
knowledge. Rule-refinement strategies (for ex-
ample, Major and Mangano [1995]) can be
used to address a related problem: The discov-
ered knowledge might be implicitly or explic-
itly redundant.

User interaction and prior knowledge:

Many current KDD methods and tools are not
truly interactive and cannot easily incorpo-
rate prior knowledge about a problem except
in simple ways. The use of domain knowl-

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sources. In addition, information agents can
be activated remotely over the network or
can trigger on the occurrence of a certain
event and start an analysis operation. Finally,
agents can help navigate and model the
World-Wide Web (Etzioni 1996), another area
growing in importance.

Uncertainty in AI

includes issues for man-

aging uncertainty, proper inference mecha-
nisms in the presence of uncertainty, and the
reasoning about causality, all fundamental to
KDD theory and practice. In fact, the KDD-96
conference had a joint session with the UAI-96
conference this year (Horvitz and Jensen 1996).

Knowledge representation

includes on-

tologies, new concepts for representing, stor-
ing, and accessing knowledge. Also included
are schemes for representing knowledge and
allowing the use of prior human knowledge
about the underlying process by the KDD
system.

These potential contributions of AI are but

a sampling; many others, including human-
computer interaction, knowledge-acquisition
techniques, and the study of mechanisms for
reasoning, have the opportunity to con-
tribute to KDD.

In conclusion, we presented some defini-

tions of basic notions in the KDD field. Our
primary aim was to clarify the relation be-
tween knowledge discovery and data mining.
We provided an overview of the KDD process
and basic data-mining methods. Given the
broad spectrum of data-mining methods and
algorithms, our overview is inevitably limit-
ed in scope: There are many data-mining
techniques, particularly specialized methods
for particular types of data and domain. Al-
though various algorithms and applications
might appear quite different on the surface,
it is not uncommon to find that they share
many common components. Understanding
data mining and model induction at this
component level clarifies the task of any da-
ta-mining algorithm and makes it easier for
the user to understand its overall contribu-
tion and applicability to the KDD process.

This article represents a step toward a

common framework that we hope will ulti-
mately provide a unifying vision of the com-
mon overall goals and methods used in
KDD. We hope this will eventually lead to a
better understanding of the variety of ap-
proaches in this multidisciplinary field and
how they fit together.

Acknowledgments

We thank Sam Uthurusamy, Ron Brachman, and
KDD-96 referees for their valuable suggestions
and ideas.

Note

1. Throughout this article, we use the term pattern
to designate a pattern found in data. We also refer
to models. One can think of patterns as compo-
nents of models, for example, a particular rule in a
classification model or a linear component in a re-
gression model.

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Usama Fayyad

is a senior re-

searcher at Microsoft Research.
He received his Ph.D. in 1991
from the University of Michigan
at Ann Arbor. Prior to joining Mi-
crosoft in 1996, he headed the
Machine Learning Systems Group
at the Jet Propulsion Laboratory
(JPL), California Institute of Tech-

nology, where he developed data-mining systems
for automated science data analysis. He remains
affiliated with JPL as a distinguished visiting scien-
tist. Fayyad received the JPL 1993 Lew Allen Award
for Excellence in Research and the 1994 National
Aeronautics and Space Administration Exceptional
Achievement Medal. His research interests include
knowledge discovery in large databases, data min-
ing, machine-learning theory and applications, sta-
tistical pattern recognition, and clustering. He was
program cochair of KDD-94 and KDD-95 (the First
International Conference on Knowledge Discovery
and Data Mining). He is general chair of KDD-96,
an editor in chief of the journal Data Mining and
Knowledge Discovery, and coeditor of the 1996 AAAI
Press book Advances in Knowledge Discovery and Da-
ta Mining.

Articles

FALL 1996 53

cal Engineering Departments at Caltech (1994) and
regularly conducts tutorials on probabilistic learn-
ing algorithms at national conferences (including
UAI-93, AAAI-94, CAIA-95, IJCAI-95). He is general
chair of the Sixth International Workshop on AI
and Statistics, to be held in 1997. Smyth’s research
interests include statistical pattern recognition, ma-
chine learning, decision theory, probabilistic rea-
soning, information theory, and the application of
probability and statistics in AI. He has published 16
journal papers, 10 book chapters, and 60 confer-
ence papers on these topics.

Gregory Piatetsky-Shapiro

is a

principal member of the technical
staff at GTE Laboratories and the
principal investigator of the
Knowledge Discovery in Databas-
es (KDD) Project, which focuses
on developing and deploying ad-
vanced KDD systems for business
applications. Previously, he

worked on applying intelligent front ends to het-
erogeneous databases. Piatetsky-Shapiro received
several GTE awards, including GTE’s highest tech-
nical achievement award for the

system for

health-care data analysis. His research interests in-
clude intelligent database systems, dependency
networks, and Internet resource discovery. Prior to
GTE, he worked at Strategic Information develop-
ing financial database systems. Piatetsky-Shapiro re-
ceived his M.S. in 1979 and his Ph.D. in 1984, both
from New York University (NYU). His Ph.D. disser-
tation on self-organizing database systems received
NYU awards as the best dissertation in computer
science and in all natural sciences. Piatetsky-
Shapiro organized and chaired the first three (1989,
1991, and 1993) KDD workshops and helped in de-
veloping them into successful conferences (KDD-95
and KDD-96). He has also been on the program
committees of numerous other conferences and
workshops on AI and databases. He edited and
coedited several collections on KDD, including two
books—Knowledge Discovery in Databases (AAAI
Press, 1991) and Advances in Knowledge Discovery in
Databases (AAAI Press, 1996)—and has many other
publications in the areas of AI and databases. He is
a coeditor in chief of the new Data Mining and
Knowledge Discovery journal. Piatetsky-Shapiro
founded and moderates the KDD Nuggets electronic
newsletter (kdd@gte.com) and is the web master for
Knowledge Discovery Mine (<http://info.gte.com/
~kdd /index.html>).

Padhraic Smyth

received a first-

class-honors Bachelor of Engi-
neering from the National Uni-
versity of Ireland in 1984 and an
MSEE and a Ph.D. from the Elec-
trical Engineering Department at
the California Institute of Tech-
nology (Caltech) in 1985 and
1988, respectively. From 1988 to

1996, he was a technical group leader at the Jet
Propulsion Laboratory (JPL). Since April 1996, he
has been a faculty member in the Information and
Computer Science Department at the University of
California at Irvine. He is also currently a principal
investigator at JPL (part-time) and is a consultant to
private industry. Smyth received the Lew Allen
Award for Excellence in Research at JPL in 1993
and has been awarded 14 National Aeronautics and
Space Administration certificates for technical in-
novation since 1991. He was coeditor of the book
Advances in Knowledge Discovery and Data Mining
(AAAI Press, 1996). Smyth was a visiting lecturer in
the Computational and Neural Systems and Electri-

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