Table Of ContentDBMiner: A System for Data Mining in
(cid:3)
Relational Databases and Data Warehouses
Jiawei Han Jenny Y. Chiang Sonny Chee Jianping Chen Qing Chen
Shan Cheng Wan Gong Micheline Kamber Krzysztof Koperski
Gang Liu Yijun Lu Nebojsa Stefanovic Lara Winstone
Betty B. Xia Osmar R. Zaiane Shuhua Zhang Hua Zhu
Data Mining Research Group, Intelligent Database Systems Research Laboratory
School of Computing Science, Simon Fraser University, British Columbia, Canada V5A1S6
URL: http://db.cs.sfu.ca/ (forresearch group) http://db.cs.sfu.ca/DBMiner (for system)
Abstract With our years of research and development
on data mining and knowledge discovery in
A data mining system, DBMiner, has been de- databases, a data mining system, DBMiner,
veloped for interactive mining of multiple-level has been developed by integration of database,
knowledgeinlargerelationaldatabasesanddata OLAPanddataminingtechnologies[6,8,9,10].
warehouses. The system implements a wide The system mines various kinds of knowledge
spectrum of data mining functions, including atmultiplelevels ofabstraction fromlargerela-
characterization, comparison, association, clas- tionaldatabasesanddatawarehouses e(cid:14)ciently
si(cid:12)cation, prediction, and clustering. By incor- and e(cid:11)ectively, with the following distinct fea-
porating several interesting data mining tech- tures:
niques, including OLAP and attribute-oriented
induction,statistical analysis,progressive deep- 1. It incorporates several interesting data
ening for mining multiple-level knowledge, and miningtechniques,includingdatacubeand
meta-rule guided mining, the system provides OLAPtechnology[3],attribute-orientedin-
auser-friendly,interactivedataminingenviron- duction [6, 9], statistical analysis, progres-
ment with good performance. sive deepening for mining multiple-level
rules[8,9,12],andmeta-ruleguidedknowl-
edge mining [5, 11]. It also implements a
1 Introduction widespectrum ofdataminingfunctions in-
cludingcharacterization, comparison,asso-
With an enormous amount of data stored in ciation, classi(cid:12)cation, prediction, and clus-
databases and data warehouses, it is increas- tering.
inglyimportantto develop powerful data ware-
2. Itperformsinteractivedataminingatmul-
housing and data mining tools for analysis of
tiple levels of abstraction on any user-
such data and mining interesting knowledge
speci(cid:12)ed setofdatainadatabaseoradata
fromit [14, 4].
warehouse using an SQL-like Data Mining
(cid:3)Research was supported in part by a research Query Language, DMQL, or a graphical user
grant and a CRD grant from the Natural Sciences interface. Users may interactively set and
and Engineering Research Council of Canada, a grant adjust various thresholds, control a data
NCE:IRIS/PrecarnfromtheNetworksofCentresofEx-
mining process, perform roll-up or drill-
cellenceofCanada,andgrantsfromB.C.AdvancedSys-
tems Institute, MPR Teltech Ltd., National Research down at multiplelevels of abstraction, and
CouncilofCanada,andHughesResearchLaboratories. generate di(cid:11)erent formsofoutputs, includ-
1
Graphical User Interface
ingcrosstabs, bar/piecharts,curves, classi-
(cid:12)cationtrees, multipleformsofgeneralized
rules, visual presentation of rules, etc.
SQL Server Discovery Modules
3. E(cid:14)cient implementation techniques have
been explored using di(cid:11)erent data struc-
tures, including multiple-dimensional data Data Concept Hierarchy
cubesandgeneralizedrelations. Theimple-
mentations have been integrated smoothly
with relational database systems and data
warehouses. Figure 1: General architecture of DBMiner
4. The data mining process may utilize user- DBMiner:Discovery Modules
or expert-de(cid:12)ned set-grouping or schema-
levelconcepthierarchieswhichcanbespec-
Characterizer Comparator Classifier
i(cid:12)ed (cid:13)exibly, adjusted dynamically based
on data distribution, and generated auto-
matically for numerical attributes. Con- Meta-Pattern
Associator Predictor
cept hierarchies are being taken as an in- Guided Miner
tegrated component of the system and are
stored as a relation in the database. Cluster Time-Series Future
Analyzer Analyzer Modules
5. Thesystemadoptsaclient/server architec-
ture and is running on Windows/NT sys-
tem. It communicates with various com-
mercial database systems for data mining
Figure 2: Knowledge discovery modules of
using the ODBC technology.
DBMiner
The system has been tested on several
medium to large relational databases, includ- Thefunctionalitiesoftheknowledgediscovery
ing NSERC (Natural Science and Engineering modulesare brie(cid:13)y described as follows:
Research Council ofCanada) research grant in-
(cid:15) The characterizer generalizes a set of task-
formation system, and U.S. City-County Data
relevant data into a generalized data cube
Book, with satisfactory performance. Addi-
which can then be used for extraction of
tional data mining modules are being designed
di(cid:11)erentkindsofrulesorbeviewedatmul-
and will be added incrementally to the system
tiplelevelsofabstractionfromdi(cid:11)erentan-
along with the progress of our research.
gles. In particular, it derives a set of char-
acteristic rules which summarizes the gen-
eralcharacteristics ofaset ofuser-speci(cid:12)ed
2 Architecture and Func-
data(calledthe target class). For example,
tionalities the symptoms of a speci(cid:12)c disease can be
summarizedby a characteristic rule.
The general architecture of DBMiner, shown in (cid:15) A comparator mines a set of discriminant
Figure1,tightlyintegratesarelationaldatabase
rules which summarize the features that
system,such as aMicrosoftSQL/Server, witha
distinguish the class being examined (the
concept hierarchy module, and a set of knowl-
target class) fromother classes (called con-
edge discoverymodules. Thediscovery modules
trasting classes). For example, to distin-
of DBMiner, shown in Figure 2, include charac-
guish one disease from others, a discrimi-
terizer, comparator,classi(cid:12)er, associator, meta-
nant rule summarizes the symptoms that
pattern guided miner, predictor, cluster ana-
discriminatethis disease fromothers.
lyzer, time series analyzer, and some planned
future modules. (cid:15) A classi(cid:12)er analyzes a set of training data
2
(i.e., a set of objects whose class label is kindsofdataanalysesfortime-relateddata
known) and constructs a model for each in the database or data warehouse, includ-
class based on the features in the data. A ingsimilarityanalysis,periodicityanalysis,
set of classi(cid:12)cation rules is generated by sequential pattern analysis, and trend and
such a classi(cid:12)cation process, which can be deviation analysis. For example, one may
used to classify future data and develop a (cid:12)nd the general characteristics of the com-
better understanding of each class in the panies whose stock price has gone up over
database. For example, one may classify 20%last year or evaluate the trend or par-
diseases and provide the symptoms which ticular growth patterns of certain stocks.
describe each class or subclass.
Another important function module of
(cid:15) An associator discovers a set of associa- DBMinerisconcept hierarchywhichprovideses-
tion rules (in the form of \A1^(cid:1)(cid:1)(cid:1)^Ai ! sential background knowledge for data general-
B1^(cid:1)(cid:1)(cid:1)^Bj")atmultiplelevelsofabstrac- ization and multiple-level data mining. Con-
tion from the relevant set(s) of data in a cept hierarchies can be speci(cid:12)ed based on the
database. For example, one may discover relationships amongdatabase attributes (called
a set of symptomsoften occurring together schema-level hierarchy) or by set groupings
with certain kinds of diseases and further (called set-grouping hierarchy) and be stored
study the reasons behind them. in the form of relations in the same database.
Moreover, they can be adjusted dynamically
(cid:15) A meta-pattern guided miner is a data
based on the distribution of the set of data rel-
mining mechanism which takes a user-
speci(cid:12)ed meta-ruleform,suchas\P(x;y)^ evanttothe dataminingtask. Also,hierarchies
Q(y;z) ! R(x;z)" as a pattern to con- for numericalattributes can be constructed au-
tomaticallybased on data distribution analysis
(cid:12)ne the search for desired rules. For exam-
[7].
ple, one may specify the discovered rules
tobeintheformof\major(s:student;x)^
P(s;y)!gpa(s;z)"inorderto(cid:12)ndthere-
3 DMQL and Interactive
lationships between a student’s major and
his/her gpa in a university database. Data Mining
(cid:15) A predictor predicts the possible values of
DBMiner o(cid:11)ers both an SQL-like data mining
some missing data or the value distribu-
query language, DMQL, and a graphical user in-
tion of certain attributes in a set of ob-
terface for interactive mining of multiple-level
jects. This involves (cid:12)nding the set of at-
knowledge.
tributes relevant to the attribute of inter-
est (by some statistical analysis) and pre-
Example 1. To characterize CS grants in the
dicting the value distribution based on the
NSERC96 database related to discipline code
set ofdatasimilartotheselected object(s).
and amount category in terms of count% and
Forexample,anemployee’spotentialsalary amount%,the query is expressed inDMQLasfol-
canbe predicted based onthesalarydistri- lows,
butionofsimilaremployeesinthecompany.
useNSERC96
(cid:15) A cluster analyzer groups a selected set of mine characteristicrulesas\CS Discipline Grants"
datainthedatabaseordatawarehouseinto with respecttodisc code, amount,
a set of clusters to ensure the interclass count%, sum(amount)%
similarity is low and intraclass similarity fromaward A, grant type G
is high. For example, one may cluster the whereA.grant code = G.grant code
housesinVancouverareaaccordingtotheir andA.disc code = \Computer Science"
house type, value, and geographical loca-
The query is processed as follows: The sys-
tion.
temcollects the relevant set of databy process-
(cid:15) A time-series analyzer performs several ing a transformed relational query, constructs
3
a multi-dimensional data cube, generalizes the
data,and then presents the outputs indi(cid:11)erent
forms, including generalized crosstabs, multi-
ple(includingvisual)formsofgeneralizedrules,
pie/bar charts, curves, etc.
A user mayinteractively set and adjust vari-
ous kindsofthresholds to controlthe datamin-
ing process. For example, one may adjust the
generalization threshold for an attribute to al-
Figure 3: A multi-dimensionaldata cube
lowmoreorlessdistinctvaluesinthisattribute.
A user may also roll-up or drill-down the gen-
eralized data at multiple levels of abstraction.
2
plesintheoriginaldatarelation. Forexample,a
generalizedrelationawardmaystoreasetoftu-
A data mining query language such as DMQL
ples, such as \award(AI;20 40k;37;835900)",
facilitates the standardization of data mining
which represents the generalized data for dis-
functions, systematic developmentof data min-
cipline code is \AI", the amount category is
ing systems, and integration with commercial
\20 40k", and such kind of data takes 37 in
relational database systems. Various kinds of
count and $835,900in (total) amount.
graphicaluserinterfacescanbedevelopedbased
A data cube can be viewed as a multi-
onsuchadataminingquerylanguage. Agraph-
dimensionalarraystructure, asshowninFigure
ical user interface facilitates interactive speci(cid:12)-
3, in which each dimension represents a gener-
cation and modi(cid:12)cationof data miningqueries,
alizedattribute andeach cellstores the valueof
concepthierarchies,andvariouskindsofthresh-
some aggregate attribute, such as count, sum,
olds,selection andchange of outputforms,roll-
etc. For example, a cube award may have two
up ordrill-down,anddynamiccontrol ofadata
dimensions: \disciplinecode"and\amountcat-
mining process. Such interfaces have been im-
egory". The value\AI"inthe \disciplinecode"
plemented in DBMiner.
dimension and \20-40k" in the \amount cate-
gory" dimension locate the corresponding val-
ues in the two aggregate attributes, count and
4 Implementation of DB-
sum, in the cube. Then the values, count% and
Miner sum(amount)%, can be derived easily.
In comparison with the generalized relation
4.1 Data structures: Generalized structure, a multi-dimensionaldata cube struc-
relation vs. multi-dimensional ture has the followingadvantages: First, itmay
data cube often save storage space since only the mea-
surement attribute values need to be stored in
Data generalization is a core function of the cube and the generalized (dimensional) at-
DBMiner. Twodatastructures, generalizedrela- tribute values willserve onlyas dimensionalin-
tion, and multi-dimensional data cube, are con- dices to the cube; second, it leads to fast access
sidered in the implementation of data general- to particular cells (or slices) of the cube using
ization. indexing structures; third, it usually costs less
to produce a cube than a generalized relation
Ageneralized relation isarelationwhichcon-
in the process of generalization since the right
sists of a set of (generalized) attributes (stor-
cell in the cube can be located easily. When a
ing generalized values of the corresponding at-
cube structure is quite sparse, the sparse cube
tributesintheoriginalrelation)andasetof\ag-
technology[1,18]shouldbe appliedtocompute
gregate" (measure) attributes (storing the val-
and store sparse cube e(cid:14)ciently.
uesresultedfromexecutingaggregatefunctions,
such as count, sum, etc.), andin which each tu- Both data structures have been implemented
ple is the result of generalization of a set of tu- in the evolution of the DBMiner system: the
4
Conceptually, a top-down, progressive deep-
ening process is preferable since it is natural
to (cid:12)rst (cid:12)nd general data characteristics at a
high abstraction level and then follow certain
interesting paths to step down to specialized
cases. However, fromthe implementationpoint
of view, it is easier to perform generalization
than specialization because generalization re-
places low level tuples by high ones through as-
cension of a concept hierarchy. Since general-
ized tuples do not register the detailed original
information, it is di(cid:14)cult to get such informa-
Figure 4: A snapshot of the output of DBMiner tion back when specialization is required later.
Characterizer Our technique which facilitates specializa-
tions on generalized relations is to save a \min-
imally generalized relation/cube" in the early
generalized relation structure is adopted in ver-
stage of generalization. That is, each attribute
sion 1.0, and a multi-dimensional data cube
inthe relevant set ofdataisgeneralized tomin-
structure without using sparse cube technology
imallygeneralized concepts (which can be done
in version 2.0. The version 3.0 adopts sparse
inonescanofthedatarelation)andthenidenti-
cube technology in the cube construction and
caltuplesinsuchageneralizedrelation/cubeare
switchesbetweenarray-basedcubeandrelation-
merged together, which derives the minimally
based cube according to its size to ensure good
generalized relation. After that, both progres-
performance in databases and data warehouses
sive deepening and interactive up-and-down can
with di(cid:11)erent sizes.
be performed with reasonable e(cid:14)ciency: If the
Besides designing good data structures, e(cid:14)-
data at the current abstraction level is to be
cient implementationof each discovery module
generalized further, generalization can be per-
has been explored, as discussed below.
formedon it directly; on the other hand, if it is
to be specialized, the desired result can be de-
rived by generalizing the minimallygeneralized
4.2 Multiple-level
relation/cube to appropriate level(s).
characterization
As shown in Figure 4, data characterization
4.3 Discovery of discriminant
summarizes and characterizes a set of task-
rules
relevant data, usually based on generalization.
For mining multiple-level knowledge, progres- The comparator of DBMiner (cid:12)nds a set of dis-
sivedeepening(drill-down)andprogressivegen-
criminant rules which distinguishes the general
eralization (roll-up) techniques can be applied.
featuresofatargetclassfromthatofcontrasting
Progressive generalization starts with a con- class(es) speci(cid:12)ed by a user. It is implemented
servative generalizationprocess which (cid:12)rst gen- as follows.
eralizes the data to slightly higher abstraction
First, the set ofrelevant datainthe database
levels than the primitivedata in the relation or
has been collected by query processing and is
data cube. Further generalizations can be per-
partitioned respectively into a target class and
formedonitprogressivelybyselectingappropri-
one or a set of contrasting class(es). Sec-
ate attributes for step-by-step generalization.
ond, attribute-oriented induction is performed
Progressive deepening starts witharelatively on the target class to extract a prime target re-
high-level generalized cube/relation, selectively lation/cube, where a prime target relation is a
and progressively specializes some of the gener- generalizedrelationinwhicheachattributecon-
alized tuples or attributes to lower abstraction tains no more than but close to the threshold
levels. value of the corresponding attribute. Then the
5
schema:
course taken=(student id;course;semester;grade):
Intra-attribute association is the association
among one or a set of attributes formed by
grouping another set of attributes in a rela-
tion. For example, the associations between
each student and his/her course performance
is an intra-attribute association because one or
a set of attributes, \course;semester;grade",
are grouped according to student id, for min-
ing associations among the courses taken by
each student. From a relational database
point of view, a relation so formed is a nested
relation obtained by nesting the attributes
Figure 5: Graphic output of the Associator of \(course;semester;grade)" with the same stu-
DBMiner dent id. Therefore, an intra-attribute associa-
tionisanassociationamongthenested itemsin
a nested relation.
conceptsinthecontrasting class(es)aregeneral-
ized to the samelevel as those inthe primetar- Inter-attribute association is the association
get relation/cube, forming the prime contrast- amonga set of attributes in a (cid:13)at relation. For
ing relation/cube. Finally, the information in example, the following is an inter-attribute as-
these two classes is used to generate qualitative sociation: the association between course and
or quantitative discriminantrules. grade, such as \the courses in computing sci-
ence tend to have good grades", etc.
Moreover, interactive drill-down and roll-up
can be performed synchronously in both target Two associations require di(cid:11)erent data min-
class and contrasting class(es) in a similar way ing techniques.
as in characterization. These functions have For mining intra-attribute associations, a
been implementedin the discriminator. data relation can be transformed into a nested
relation in which the tuples which share the
samevaluesinthenestingattributesaremerged
into one. For example, the course taken rela-
4.4 Multiple-level association
tion can be folded into a nested relation with
the schema,
Based on many studies on e(cid:14)cient mining of
associationrules [2,17,8],amultiple-levelasso-
course taken = (student id;course history)
ciationruleminer(called\associator")hasbeen course history = (course;semester;grade).
implemented in DBMiner. An output of the as-
sociator is shown in Figure 5. Withsuch transformation,itiseasytoderive
Di(cid:11)erent from mining association rules in association rules like \90% senior CS students
transaction databases, a relational associator tend to take at least three CS courses at 300-
may (cid:12)nd two kinds of associations: inter- level or up in each semester". Since the nested
attribute association and intra-attribute associ- tuples (or values) can be viewed as data items
ation. The former is an association among dif- inthesametransaction,themethodsformining
ferent attributes; whereas the latter is an asso- association rules in transaction databases, such
ciation within one or a set of attributes formed as [2, 8], can be applied to such transformed
bygroupingofanother set ofattributes. Thisis relations in relationaldatabases.
illustrated in the followingexample. The multi-dimensional data cube structure
facilitates e(cid:14)cient mining of multi-level, inter-
Example 2. Suppose the \course taken" rela- attribute association rules. A count cell of
tion in a university database has the following a cube stores the number of occurrences of
6
the corresponding multi-dimensional data val-
ues; whereas a dimension count cell stores the
sumofcountsofthewholedimension. Withthis
structure, it is straightforward to calculate the
measurements such as support and con(cid:12)dence
ofassociation rules based onthe values inthese
summary cells. A set of such cubes, ranging
from the least generalized cube to rather high
levelcubes, facilitateminingofassociationrules
2
at multiplelevels of abstraction.
Figure 6: Classi(cid:12)er of DBMiner
4.5 Meta-rule guided mining
Since there are many ways to derive associa-
in such a database, as illustrated below:
tion rules in relational databases, it is prefer-
able to have users to specify some interest-
major(s;\Science")^gpa(s;\Excellent")!
ing constraints to guide a data miningprocess.
status(s;\Graduate") (60%)
Such constraints can be speci(cid:12)ed in ameta-rule
major(s;\Physics")^status(s;\M:Sc") !
(or meta-pattern) form [16], which con(cid:12)nes the
gpa(s;\3:8 4:0") (76%)
search to speci(cid:12)c forms of rules. For exam-
ple, a meta-rule \P(x;y) ! Q(x;y;z)", where
The mining of such multi-level rules can
P and Q are predicate variables matching dif-
be implemented in a similar way as min-
ferent properties in a database, can be used as
ing multiple-level association rules in a multi-
a rule-formconstraint in the search.
2
dimensionaldata cube [11].
Inprinciple,ameta-rulecan beused toguide
the mining of many kinds of rules. Since
the association rules are in the form similar 4.6 Classi(cid:12)cation
to logic rules, we have (cid:12)rst studied meta-rule
guided mining of association rules in relational Dataclassi(cid:12)cationistodevelop adescription or
databases [5]. Di(cid:11)erent from the study by modelforeachclass inadatabase,based onthe
[16] where a meta-predicate may match any featurespresent inasetofclass-labeledtraining
relation predicates, deductive predicates, at- data.
tributes,etc.,wecon(cid:12)nethematchtoonlythose There have been many data classi(cid:12)cation
predicates corresponding to the attributes in a methods studied, including decision-tree meth-
relation. One such exampleis illustrated as fol- ods,suchasID-3andC4.5[15],statisticalmeth-
lows. ods, neural networks, rough sets, etc. Recently,
some database-oriented classi(cid:12)cation methods
Example 3. A meta-rule guided data mining have also been investigated [13].
query can be speci(cid:12)ed in DMQL as follows for
Our classi(cid:12)cation method consists of four
mining a speci(cid:12)c form of rules related to a set
steps: (1) collection of the relevant set of data
of attributes: \major, gpa, status, birth place,
address" in relation student for those born in and partitioning of the data into training and
Canada in a university database. testing data,(2) analysisofthe relevance ofthe
attributes,(3)construction ofclassi(cid:12)cation(de-
mine associations cision) tree, and (4) test of the e(cid:11)ectiveness of
with respectto major,gpa, status, birth place, addressthe classi(cid:12)cation using the test data set.
from student
Attribute relevance analysis is performed
where birth place =\Canada"
set rule template based on the analysis of an uncertainty mea-
major(s:student;x)^Q(s;y) ! R(s;z) surement,ameasurementwhichdetermineshow
much an attribute is in relevance to the class
Multi-levelassociationrulescanbediscovered attribute. Several top-most relevant attributes
7
retain for classi(cid:12)cation analysis whereas the The generalization-based multi-level classi(cid:12)-
weakly or irrelevant attributes are not consid- cationprocesshasbeenimplementedintheDB-
ered in the subsequent classi(cid:12)cation process. Miner system. An output of the DBMiner clas-
si(cid:12)er is shown in Figure 6.
In the classi(cid:12)cation process, our classi(cid:12)er
adopts a generalization-based decision-tree in-
duction method which integrates attribute-
oriented induction and OLAP data cube tech- 4.7 Prediction
nologywithadecision-tree inductiontechnique,
by (cid:12)rst performing minimal generalization on A predictor predicts data values or valuedistri-
the set of training data to generalize attribute butions on the attributes of interest based on
values in the training set, and then performing similargroups of data in the database. For ex-
decision tree induction on the generalized data. ample,one maypredict the amount of research
grants that an applicant may receive based on
Since a generalized tuple comes from the
thedataaboutthesimilargroupsofresearchers.
generalization of a number of original tuples,
the count information is associated with each The power of data prediction should be con-
generalized tuple and plays an important role (cid:12)nedtotherangesofnumericaldataorthenom-
in classi(cid:12)cation. To handle noise and excep- inaldatageneralizabletoonlyasmallnumberof
tional data and facilitate statistical analysis, categories. It is unlikely to give reasonable pre-
two thresholds, classi(cid:12)cation threshold and ex- diction on one’s name or social insurance num-
ception threshold, are introduced. The former ber based on other persons’ data.
helps justi(cid:12)cation ofthe classi(cid:12)cation ata node For successful prediction, the factors (or at-
when a signi(cid:12)cant set of the examples belong tributes) which strongly in(cid:13)uence the values of
to the same class; whereas the latter helps ig- the attributes of interest should be identi(cid:12)ed
nore anode inclassi(cid:12)cationifitcontainsonlya (cid:12)rst. This can be done by the analysis of data
negligiblenumber of examples. relevance or correlationsby statisticalmethods,
There are several alternatives for doing gen- decision-tree classi(cid:12)cationtechniques, orsimply
eralization before classi(cid:12)cation: A data set can be based on expert judgement. To analyze at-
be generalized toeither aminimallygeneralized tribute relevance, the uncertainty measurement
abstraction level, an intermediate abstraction similar to the method used in our classi(cid:12)er is
level, or a rather high abstraction level. Too applied. This process ranks the relevance of
low an abstraction level mayresult in scattered all the attributes selected and only the highly
classes, bushy classi(cid:12)cation trees, and di(cid:14)culty ranked attributes will be used in the prediction
at concise semantic interpretation; whereas too process.
high a level may result in the loss of classi(cid:12)ca- After the selection of highly relevant at-
tion accuracy. tributes, a generalized linear model has been
Currently, we are testing several alternatives constructed which can be used to predict the
at integration of generalization and classi(cid:12)ca- value or value distribution of the predicted at-
tion in databases, such as (1) generalize data tribute. If the predicting attribute is a numeri-
to some medium abstraction levels; (2) gener- cal data, a set of curves are generated, each in-
alize data to intermediate abstraction level(s), dicatingthe trend of likelychanges of the value
and then perform node merge and split for bet- distribution of the predicted attribute. If the
ter class representation and classi(cid:12)cation accu- predicting attribute is a categorical data, a set
racy; and (3) perform multi-level classi(cid:12)cation of pie charts are generated, each indicating the
andselect adesired levelbyacomparisonofthe distributionsofthevaluerangesofthepredicted
classi(cid:12)cation quality at di(cid:11)erent levels. Since attribute.
all three classi(cid:12)cation processes are performed When a query probe is submitted, the corre-
in relatively small, compressed, generalized re- sponding value distributionof the predicted at-
lations, it is expected to result in e(cid:14)cient clas- tributecanbeplottedbasedonthecurvesorpie
si(cid:12)cation algorithmsin large databases. chartsgeneratedabove. Therefore,thevaluesin
the set of highly relevant predicting attributes
8
discover distinct groups in their customer bases
and develop targeted marketingprograms.
Dataclustering hasbeen studiedinstatistics,
machinelearninganddataminingwithdi(cid:11)erent
methods and emphases. Many clustering meth-
ods havebeen developed and appliedto various
domains, such as data classi(cid:12)cation and image
processing.
Dataminingapplicationsdealwithlargehigh
dimensional data, and frequently involve cate-
goricaldomainswith concept hierarchies. How-
ever, mostof the existing data clustering meth-
Figure7: Predictionoutputwhenthepredicting
ods can only handle numeric data, or can not
attribute is a numeric one
produce good quality results in the case where
categorical domainsare present.
Our cluster analyzer is based on the well-
known k-means paradigm. Comparing to the
other clustering methods, the k-means based
methods are promising for their e(cid:14)ciency in
processing large data sets. However, their use
is often limitedto numericdata. Toadequately
re(cid:13)ect categorical domains, we have developed
amethod ofencoding concept hierarchies. This
enablesustode(cid:12)ne adissimilaritymeasurethat
not only takes into account both numeric and
categorical attributes, but also at multiple lev-
els. Due to these modi(cid:12)cations, our cluster an-
Figure8: Predictionoutputwhenthepredicting
alyzer can cluster large data sets with mixed
attribute is a categorical one
numericandcategoricalattributesinawaysim-
ilar to k-means. It can also perform multi-level
clustering and select a desired level by a com-
can be used for trustable prediction.
parisonofthe clusteringqualityatdi(cid:11)erent lev-
Figure 7 shows the prediction output when
els. On the other hand, the user or the analyst
the predicting attribute is a numeric one;
candirecttheclusteringprocessbyeitherselect-
whereas Figure 8 shows the prediction output
ingaset ofrelevant attributes forthe requested
when the predicting attribute is a categorical
clustering query, or assigninga weight factor to
one.
each attribute, or both, so that increasing the
weight of an attribute increases the likelihood
thatthealgorithmwillclusteraccordingtothat
4.8 Clustering
attribute.
Data clustering, also viewed as \unsupervised
learning", is a process of partitioning a set of
data into a set of classes, called clusters, with 5 Further Development of
the members of each cluster sharing some in-
DBMiner
teresting common properties. A good cluster-
ing method will produce high quality clusters,
TheDBMinersystemiscurrentlybeingextended
in which the intra-class (i.e., intra-cluster) sim-
in several directions, as illustrated below.
ilarityis high and inter-class similarityis low.
Clustering has manyinteresting applications. (cid:15) Furtherenhancementofthepowerande(cid:14)-
For example, it can be used to help marketers ciencyofdatamininginrelationaldatabase
9
systemsanddatawarehouses,includingthe mining and knowledge discovery in large
improvement of system performance and databases"), and has served or is cur-
rule discovery qualityforthe existingfunc- rentlyservingintheprogramcommitteesof
tional modules, and the development of over30internationalconferences andwork-
techniques for mining new kinds of rules, shops, including ICDE’95 (PC vice-chair),
especially on time-related data. DOOD’95, ACM-SIGMOD’96, VLDB’96,
KDD’96 (PC co-chair), CIKM’97, SSD’97,
(cid:15) Furtherenhancementoftheperformanceof
KDD’97, and ICDE’98. He has also been
data mining in large databases and data
serving as an editor for IEEE Transactions
warehouses by exploration of parallel pro-
onKnowledgeandDataEngineering,Jour-
cessing using NT clusters. Some algorithm
nalofIntelligentInformationSystems,and
design and experimental work have been
Data Mining and Knowledge Discovery.
performed in this direction.
(cid:15) Integration, maintenance and application (cid:15) Jenny Y. Chiang received her B.Sc. de-
greeinComputingScience atSimonFraser
of discovered knowledge, including incre-
University. Currently, she is working on
mentalupdate of discovered rules, removal
the design and implementationof the DB-
ofredundantorlessinterestingrules,merg-
Miner system. Her research interests are
ing of discovered rules into a knowledge-
data cube construction, OLAP implemen-
base, intelligentquery answering using dis-
tation,and association rule mining.
covered knowledge,andthe construction of
multiplelayered databases.
(cid:15) SonnyCheereceived hisMASc. degreein
(cid:15) Extension of data mining technique to- Aerospace at University of Toronto. Cur-
wards advanced and/or special purpose rently,he is a Ph.D.candidate of School of
database systems, including extended- Computing Science, Simon Fraser Univer-
relational,object-oriented, textual, spatial, sity. He is working on the design and im-
temporal, multi-media, and heterogeneous plementation of the DBMiner System. His
databases and Internet information sys- research interests are active database min-
tems. Currently, three such data mining ing,OLAPimplementation,andtime-series
systems, GeoMiner, LibMiner, and Web- data mining.
Miner, for mining knowledge in spatial
databases,librarydatabases,andtheInter- (cid:15) Jianping Chen received his B. Sc. in
netinformation-baserespectively,arebeing Mathematics in the University of Science
under design and construction. and Technology of China in 1987, and
M.Sc. degree in Mathematics at Simon
FraserUniversityin1996. Heworkedinthe
About the Authors Institute of Applied Mathematics, Chinese
Academy of Sciences, Beijing, China from
(cid:15) Jiawei Han (Ph.D. Univ. of Wiscon- 1987to 1994. Currently, he is a M.Sc. stu-
sin at Madison, 1985), Professor of the dent in the School of Computing Science,
School of Computing Science and Direc- SimonFraser University. He is working on
tor of Intelligent Database Systems Re- paralleldatabase mining,and data classi(cid:12)-
search Laboratory, Simon Fraser Univer- cation techniques.
sity, Canada. He has conducted research
in the areas of data mining and data (cid:15) Qing Chen received her B.Sc. degree in
warehousing,deductiveandobject-oriented Computer Science from the University of
databases, spatial databases, multimedia Science and Technology of China, in 1993.
databases, and logic programming, with She is currently working on the M.Sc. de-
over 100 journal and conference publi- gree at School of Computing Science, Si-
cations. He is a project leader of the mon Fraser University. Her research inter-
Canada NCE/IRIS:HMI-5 project (\data ests include spatial data mining.
10
