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1、PROCEEDINGSOFWORLDACADEMYOFSCIENCE,ENGINEERINGANDTECHNOLOGYVOLUME9NOVEMBER2005ISSN1307-6884DynamicClusteringusingParticleSwarmOptimizationwithApplicationinUnsupervisedImageClassificationMahamedG.H.Omran,AndriesPEngelbrecht,andAyedSalmanHowever,hierarchic
2、alclusteringtechniquessufferfromtheAbstract—Anewdynamicclusteringapproach(DCPSO),basedfollowingdrawbacks:onParticleSwarmOptimization,isproposed.ThisapproachisñTheyarestatic,i.e.datapointsassignedtoaclusterappliedtounsupervisedimageclassification.Thepropo
3、sedapproachcannotmovetoanothercluster.automaticallydeterminesthe"optimum"numberofclustersandñTheymayfailtoseparateoverlappingclustersduetoasimultaneouslyclustersthedatasetwithminimaluserinterference.lackofinformationabouttheglobalshapeorsizeoftheThealgor
4、ithmstartsbypartitioningthedatasetintoarelativelylargenumberofclusterstoreducetheeffectsofinitialconditions.Usingclusters.binaryparticleswarmoptimizationthe"best"numberofclustersisselected.ThecentersofthechosenclustersisthenrefinedviatheK-Ontheotherhand,
5、partitionalclusteringalgorithmsmeansclusteringalgorithm.Theexperimentsconductedshowthatpartitionthedatasetintoaspecifiednumberofclusters.Thesetheproposedapproachgenerallyfoundthe"optimum"numberofalgorithmstrytominimizecertaincriteria(e.g.asquareerrorclus
6、tersonthetestedimages.function)andcanthereforebetreatedasoptimizationproblems.TheadvantagesofhierarchicalalgorithmsaretheKeywords—ClusteringValidation,ParticleSwarmOptimization,UnsupervisedClustering,UnsupervisedImageClassification.disadvantagesofthepart
7、itionalalgorithmsandviceversa.PartitionalclusteringtechniquesaremorepopularthanI.INTRODUCTIONhierarchicaltechniquesinpatternrecognition[2],hence,thispaperwillconcentrateonpartitionaltechniques.ATAclusteringistheprocessofidentifyingnaturalDgroupingsorclus
8、ters,withinmultidimensionaldata,Partitionalclusteringaimstooptimizeclustercenters,aswellasthenumberofclusters[10].Mostclusteringbasedonsomesimilaritymeasure(e.g.Euclideandistance)algorithmsrequirethenumberofclusterstobespe
