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1、南京大學(xué)學(xué)報(bào)(自然科學(xué))第50卷第4期Vo1．50，No．4JOURNALOFNANJINGUNIVERSITY2014年7月July，2014(NATURALSCIENCES)全粒度聚類算法李飛江卜，成紅紅，錢(qián)宇華(1．山西大學(xué)計(jì)算機(jī)與信息技術(shù)學(xué)院，太原，030006；2．山西大學(xué)數(shù)學(xué)科學(xué)學(xué)院，太原，030006)摘要：聚類分析是數(shù)據(jù)挖掘與知識(shí)發(fā)現(xiàn)領(lǐng)域的一個(gè)重要研究方向．多數(shù)聚類算法中相似性是其核心概念之一，對(duì)象之間的相似性會(huì)被直接或者間接的計(jì)算出來(lái)．傳統(tǒng)的相似性度量方法多是基于單一的粒度去觀察兩個(gè)被測(cè)對(duì)象．在
2、人類認(rèn)知過(guò)程中，通常采用多粒度來(lái)更合理有效地進(jìn)行問(wèn)題求解．本文借鑒人類的這種多粒度認(rèn)知機(jī)理，提出一種新的相似性學(xué)習(xí)方法，稱作全粒度相似性度量方法，基于此發(fā)展了一種全粒度聚類算法．而全粒度相似性度量從各個(gè)角度觀察被測(cè)對(duì)象，進(jìn)而會(huì)得到兩個(gè)對(duì)象間更加真實(shí)的相似度．從UCI數(shù)據(jù)集中選取5組數(shù)據(jù)進(jìn)行實(shí)驗(yàn)，最后通過(guò)與兩種傳統(tǒng)的聚類方法比較驗(yàn)證了全粒度聚類算法的合理性與有效性．關(guān)鍵詞：相似性度量，聚類分析，全粒度Whole—granulationclusteralgorithmLiFeijiang，ChengHonghong，
3、QianYuhua(1．SchoolofComputerandInformationTechnology，ShanxiUniversity，Taiyuan，03006，China；2．SchoolofMathematics，ShanxiUniversity，Taiyuan，03006，China)Abstract：Inclusteranalysis，especiallyclusterinanoptimizationprocess，oneofthedecisivefactorsisthesimilaritymeas
4、ureemployedintheclusteringcriterionfunction．Byfar，allproposedclustermethodshavetoassumeconnectionamongtheinformationobjectsthatappliedon．Similaritybetweeneverypairobjectsshouldbecomputed，therearetwochoiceswhichdefinedasexplicitlyorimplicitly．Henceweatherthest
5、ructureofdatacanbedescribedbythesimilaritymeasurecorrectlydeterminestheeffectivenessofaclusteringalgorithm．Inaddition，asoneofimportantcharactersinhuman'scognition，multi—granulationcognitionplaysakeyrolefordatamodeling．Onaccountoffrommulti—perspectiveandmulti—
6、leveltoparseoneproblem，multgranulationanalysiscanobtainmorereasonableandmoresatisfiedsolutions．Throughreferencinghumansmulti—granulationcognitiveability，inthispaper，weintroducedanovelsimilaritymeasurecalledwhole—granulationsimilaritymeasureandapplythissimilar
7、itymeasureintoclusteringcriterionfunctiontogetaclusteralgorithmcalledwhole—granulationclusteralgorithminordertoverifytherationalizationofwhole_granulati0nsimilaritymeasure．Thetraditionaldissimilarity／similaritymeasureexerciseonlyonesingleviewpoints，usuallyist
8、heorigin．Moreinformativeassessmentofsimilaritycouldbeachievedbecausewholegranulationtakesallsidesintoconsideration．Asaleadingpartitionalclusteringtechnique，kmcansisoneofthemostfavoritealg
