Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics

298 Pages·2011·5.69 MB·English

by  Ujjwal Maulik

Multiobjective Genetic Algorithms for Clustering
•
Ujjwal Maulik (cid:129) Sanghamitra Bandyopadhyay
Anirban Mukhopadhyay
Multiobjective
Genetic Algorithms
for Clustering
Applications in Data Mining
and Bioinformatics
123
Prof.UjjwalMaulik Prof.SanghamitraBandyopadhyay
DepartmentofComputerScience MachineIntelligenceUnit
andEngineering IndianStatisticalInstitute
JadavpurUniversity B.T.Road203
700032Kolkata 700108Kolkata
WestBengal WestBengal
India India
[email protected] [email protected]
Dr.AnirbanMukhopadhyay
DepartmentofComputerScience
andEngineering
UniversityofKalyani
741235Kalyani
Nadia,WestBengal
India
[email protected]
ISBN978-3-642-16614-3 e-ISBN978-3-642-16615-0
DOI10.1007/978-3-642-16615-0
SpringerHeidelbergDordrechtLondonNewYork
LibraryofCongressControlNumber:2011932492
ACMCodes:I.2,J.3,H.2,H.3
(cid:2)c Springer-VerlagBerlinHeidelberg2011
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SpringerispartofSpringerScience+BusinessMedia(www.springer.com)
To myparentsManojKumar Maulikand
GouriMaulik,mysonUtsav,andmystudents
UjjwalMaulik
To myparentsSatyendraNath Banerjeeand
BandanaBanerjee, mysonUtsav,andmy
students
SanghamitraBandyopadhyay
To myparentsSomnathMukhopadhyayand
ManjusriMukhopadhyay,my wifeAnindita,
andmysonAgnibho
AnirbanMukhopadhyay
•
Preface
Clusteringisanimportantunsupervisedclassificationtechniquewhereasetofpat-
terns, usually vectors in multidimensional space, are grouped into clusters based
on some similarity or dissimilarity criteria. In crisp clustering, each pattern is as-
signed to exactly one cluster, whereas in fuzzy clustering, each pattern is given a
membershipdegreeto each class. Fuzzy clustering is inherentlymoresuitable for
handlingimpreciseandnoisydatawithoverlappingclusters.Clusteringtechniques
aim to find a suitable groupingof the input dataset so that some criteria are opti-
mized.Hencetheproblemofclusteringcanbe posedasanoptimizationproblem.
The objectivesto be optimizedmay representdifferentcharacteristicsof the clus-
ters, such as compactness, separation, and connectivity.A straightforward way to
poseclusteringasanoptimizationproblemistooptimizesomeclustervalidityin-
dexthatreflectsthegoodnessoftheclusteringsolutions.Allpossiblepartitionings
ofthedatasetandthecorrespondingvaluesofthevalidityindexdefinethecomplete
search space. Traditional partitional clustering techniques, such as K-means and
fuzzyC-means,employgreedysearchtechniquesoverthesearchspacetooptimize
thecompactnessoftheclusters.Althoughthesealgorithmsarecomputationallyeffi-
cient,theyoftengetstuckatsomelocaloptimadependingonthechoiceoftheinitial
clustercenters.Moreover,theyoptimizeasingleclustervalidityindex(compactness
inthiscase),andthereforedonotcoverdifferentcharacteristicsofthedatasets.
To overcomethe problemoflocaloptima,someglobaloptimizationtoolssuch
as Genetic Algorithms(GAs) havebeen widely usedto reachthe globaloptimum
valueofthechosenvaliditymeasure.GAsarerandomizedsearchandoptimization
techniques guided by the principles of evolution and natural genetics, and have a
large amountof implicit parallelism. GAs perform multimodalsearch in complex
landscapesandprovidenear-optimalsolutionsforthe objectiveorfitness function
ofanoptimizationproblem.ConventionalGA-basedclusteringtechniquesusesome
validity measure as the fitness value. However, no single validity measure works
equally well for different kinds of datasets. Thus it is natural to simultaneously
optimizemultiplesuchmeasuresforcapturingdifferentcharacteristicsofthedata.
vii
viii Preface
Simultaneous optimization of multiple objectives provides improved robust-
ness to different data properties. Hence it is useful to utilize multiobjective GAs
(MOGAs) for clustering. In multiobjective optimization (MOO), search is per-
formed over a number of often conflicting objective functions. In contrast to sin-
gle objective optimization, which yields a single best solution, in MOO the final
solution set contains a number of Pareto-optimalsolutions, none of which can be
furtherimprovedwithregardtoanyoneobjectivewithoutdegradinganother.Mul-
tiobjective clustering techniquesoptimize more than one cluster validity index si-
multaneously,leadingtohigh-qualityresults.Astherelativeimportanceofdifferent
clustering criteria is unknown, it is better to optimize them separately rather than
combinetheminasinglemeasuretobeoptimized.Theresultantsetofnear-Pareto-
optimalsolutionscontainsanumberofnon-dominatedsolutions,whichtheusercan
judgerelativelytopickthemostpromisingonefortheproblemrequirements.
Data mining involves discovering interesting and potentially useful patterns of
differenttypessuchasassociations,summaries,rules,changes,outliersandsignif-
icantstructures.Clusteringisan importantunsuperviseddataminingtoolthatcan
beusefulinminingdifferentkindsofdatasets,includinggeoscientificdata,biolog-
icaldata,WorldWideWebdata,andmultimediadata.Inthepresentbook,wehave
explored the application of multiobjective fuzzy clustering in discovering knowl-
edge from geoscientific data such as satellite images and biological data such as
microarraygeneexpressiondata.
Remote sensing satellite images have significant applications in differentareas
suchasclimatestudies,theassessmentofforestresources,andtheexaminationof
marine environments. For remote sensing applications, classification is an impor-
tant task where the pixels in the images are classified into homogeneousregions,
eachofwhichcorrespondstosomeparticularlandcovertype.Theproblemofpixel
classification is often posed as that of clustering in the intensity space. Clustering
techniquesusedforpixelclassificationshouldbeabletohandlethesatelliteimages
thathavealargenumberofclasseswithoverlappingandnonlinearclassboundaries.
Inasatelliteimage,apixelrepresentsanareaoflandspace,whichmaynotneces-
sarilybelongtoasinglelandcovertype.Hence,itisevidentthatalargeamountof
imprecisionanduncertaintycanbeassociatedwiththepixelsinaremotelysensed
image.Therefore,itisappropriateaswellasnaturaltoapplytheprinciplesoffuzzy
settheoryinthedomainofpixelclassification.Inthisbook,weexploretheappli-
cationofmultiobjectivefuzzyclusteringalgorithmsforclassifyingremotesensing
imageryinordertodemonstratetheireffectiveness.
Inrecentyears,advancesinthefieldofmolecularbiology,coupledwithadvances
ingenomictechnologies,haveledtoanexplosivegrowthinthebiologicalinforma-
tion generated by the scientific community. Bioinformatics, viewed as the use of
computationalmethodsto makebiologicaldiscoveries,hasevolvedasa majorre-
search directionin responseto this delugeof information.The main purposeis to
utilizecomputerizeddatabasestostore,organizeandindexthedata,andspecialized
toolstoviewandanalyzethedata.Itisaninterdisciplinaryfieldinvolvingbiology,
Preface ix
computer science, mathematics and statistics to analyze biological sequence data
andgenomecontentand arrangement,and to predictthe functionandstructureof
macromolecules. The ultimate goal of the field is to enable the discovery of new
biological insights as well as to create a global perspective from which unifying
principles in biology can be derived. Data analysis tools used earlier in bioinfor-
matics were mainly based on statistical techniqueslike regression and estimation.
Thelandscapeofbiologicalandbiomedicalresearchisbeingchangedrapidlywith
theinventionofmicroarrays,whichenablesimultaneousviewsofthetranscription
levelsofalargenumberofgenesacrossdifferenttissuesamplesortimepoints.Mi-
croarraytechnologyhasapplicationsintheareasofmedicaldiagnosis,biomedicine,
andgeneexpressionprofiling,amongothers.Clusteringisusedtoidentifythesets
of genes with similar expression profiles. Clustering algorithms are also used for
classifyingdifferenttissuesamplesrepresentingmultiplecancertypesandforiden-
tifyingpossiblegenemarkers.Anotherimportantmicroarrayanalysistoolisbiclus-
tering,whichaimstodiscoverlocalpatternsfrommicroarraydatasetsthatcontain
asubsetofgenesco-expressedinasubsetofexperimentalconditions.Biclustering
reflects the biological reality better than does the traditional clustering approach.
The present volume explores the application of multiobjective GAs in clustering
and biclustering in microarray gene expression datasets along with the biological
relevanceoftheexperimentalresults.
Anotherimportantareathatisaddressedinthisbookiscategoricaldatacluster-
ing.Manyreal-lifedatasetsarecategoricalinnature,i.e.,nonaturalorderingamong
thevaluesofaparticularattributecanbefound.Hence,fordatasetshavingcategori-
calattributedomains,thereisnoinherentdistancemeasurebetweenanytwooftheir
points.Also,themeanofasetofpointscannotbecomputedinsuchdatasets.Thus
theconventionalclusteringalgorithmsdonotworkwithcategoricaldatasets.With
thegrowingdemandforcategoricaldataclustering,afewclusteringalgorithmsfo-
cussingoncategoricaldatahavebeendevelopedinthepastfewyears.Thisvolume
addressestheissuesindesigningmultiobjectiveclusteringalgorithmsforcategori-
calattributeswithapplicationstoartificialandreal-lifecategoricaldatasets.
Thepresentvolumeisaimedatprovidingatreatiseinaunifiedframework,with
extensiveapplicationstoreal-lifedatasets.Chapter1introducestheclusteringprob-
lemanddiscussesdifferenttypesofclusteringalgorithms,clustervaliditymeasures,
research issues, challenges and applications, and representation of the clustering
problemasanoptimizationtaskandthepossibleuseofGAsandMOGAsforthis
purpose.Chapter2describesthebasicprinciplesofGAsandMOGAs,theirtheo-
reticalanalysisandtheirapplicationstodataminingandbioinformaticsproblems.
Chapter 3 gives a broad overview of data mining and knowledge discovery tasks
and applications. Chapter 4 provides an introduction to basic molecular biology
concepts followed by basic tasks in bioinformatics, with a discussion on some of
the existingworks.Chapter5 presentsa multiobjectivefuzzyclusteringalgorithm
that uses real-valued center-based encoding of chromosomes and simultaneously
optimizes two fuzzy cluster validity indices. In Chapter 6, a method based on the