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基于改进粒子群的K-means聚类算法.docx

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基于改进粒子群的K-means聚类算法 Title:ImprovedParticleSwarmOptimizationforK-meansClusteringAlgorithm Abstract: Clusteringisafundamentaltaskindataminingandmachinelearning,whichaimstopartitiondatapointsintogroupsbasedontheirsimilarities.K-meansisapopularclusteringalgorithmknownforitssimplicityandefficiency.However,traditionalK-meansalgorithmhaslimitations,suchasrelianceoninitialcentroidsandtheriskofgettingstuckinlocaloptima.Toovercometheselimitations,thispaperproposesanimprovedParticleSwarmOptimization(PSO)algorithmforK-meansclustering. Introduction: TheK-meansalgorithmisawidelyusedclusteringalgorithmthataimstominimizethesumofsquareddistancesbetweendatapointsandtheirassignedcentroids.However,theperformanceofK-meansheavilydependsontheinitialselectionofcentroids,whichcanresultindifferentclusterings.Moreover,K-meansoftenstruggleswithfindingtheglobaloptimaduetoitsdeterministicnature.ThispaperintroducesanimprovedPSOalgorithmtoenhancetheperformanceoftheK-meansalgorithm. ImprovedParticleSwarmOptimizationAlgorithm: ParticleSwarmOptimizationisapopulation-basedoptimizationalgorithminspiredbythebehaviorofbirdflockingorfishschooling.IntraditionalPSO,particlesmovethroughthesearchspacebyadjustingtheirpositionsandvelocitiesbasedontheirownbest-knownpositionandtheglobalbest-knownpositionofthepopulation.IntheimprovedPSOalgorithm,weintroduceanewupdatestrategyforparticlepositions,velocities,andpersonalbest-knownpositionstooptimizetheK-meansclustering. 1.InitialCentroidGeneration: IntheimprovedPSOalgorithm,theinitialcentroidsaregeneratedusingamodifiedversionofthetraditionalK-means++initialization.Thishelpstoenhancethequalityoftheinitialcentroidsandreducestherelianceonrandominitialization. 2.EnhancedParticleMovement: ToovercomethelimitationsoftraditionalPSO,weintroducetwoenhancementstotheparticlemovementprocess.First,weincorporatealocalbest-knownpositionforeachparticle,whichenablesparticlestoexploittheirpreviousbest-knownpositions.Second,weemployavelocityclampingmechanismtolimittheexcessivemovementofp