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基于高斯混合模型的改进的减法聚类算法.docx

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基于高斯混合模型的改进的减法聚类算法 Introduction GaussianMixtureModel(GMM)isastatisticalmodelusedinunsupervisedlearningforrepresentingthedistributionofdatainamulti-dimensionalspace.ThemodelassumesthateachdatapointisgeneratedfromamixtureofGaussiandistributionswithunknownparameters,anditestimatestheseparametersusingtheExpectation-Maximization(EM)algorithm.TheGMMmodelhasbeenwidelyusedinimagesegmentation,clustering,andothermachinelearningtasks. Inthispaper,weproposeanimprovedsubtractionclusteringalgorithmbasedontheGMMmodel.Thebasicideaofthesubtractionclusteringalgorithmistoremoveasubsetofdatapointsfromtheoriginaldatasetandclustertheremainingpoints.Theremovedsubsetisthenclusteredseparately,andthetwosetsofclustersaremergedtoformthefinalresult.TheimprovedalgorithmusestheGMMmodeltoclusterthedatapoints,anditintegratesaweightingschemetoadjustthecontributionofeachGaussiancomponenttothefinalclusteringresult. Background Subtractionclusteringisawell-knowntechniqueinclusteranalysis,whichinvolvesdividingtheoriginaldatasetintotwosubsets-areferencesetandasubtractionset.Thereferencesetisusedforclustering,whilethesubtractionsetissubtractedfromthereferencesettoobtainamodifiedsetofdatapoints.Themodifiedsetisthenclusteredagain,andthetwosetsofclustersaremerged.Thisprocessisrepeateduntilthedesirednumberofclustersisobtained. However,theperformanceofthesubtractionclusteringalgorithmdependsonthequalityofthereferencesetandthesubtractionset.Ifthereferencesetcontainsnoiseorirrelevantdata,theresultingclustersmaynotbeaccurate.Moreover,thesubtractionprocessmayremoveimportantinformationfromtheoriginaldataset,leadingtoalossofinformation. GaussianMixtureModel(GMM)isamorepowerfulmodelthanthetraditionalK-meansalgorithm.ItcanmodelthedatadistributionwithaGaussianmixture,whichisusuallymoreflexibleandaccuratethanK-means.Moreover,Gaussianmixturemodelingcanhandlenon-sphericalclustersandcanestimatethecovariancestructureofthedata. TheproposedalgorithmcombinestheadvantagesofGMMclusteringandsubtractionclusteringtoimprovetheperform