Soumeya Zerabi, S. Meshoul, Samia Boucherkha
{"title":"基于Hadoop-MapReduce的内部聚类验证索引模型","authors":"Soumeya Zerabi, S. Meshoul, Samia Boucherkha","doi":"10.4018/ijdst.2020070103","DOIUrl":null,"url":null,"abstract":"Clustervalidationaimstobothevaluatetheresultsofclusteringalgorithmsandpredictthenumberof clusters.Itisusuallyachievedusingseveralindexes.Traditionalinternalclusteringvalidationindexes (CVIs)aremainlybasedincomputingpairwisedistanceswhichresultsinaquadraticcomplexity oftherelatedalgorithms.TheexistingCVIscannothandlelargedatasetsproperlyandneedtobe revisitedtotakeaccountoftheever-increasingdatasetvolume.Therefore,designofparalleland distributedsolutionstoimplementtheseindexesisrequired.Tocopewiththisissue,theauthors proposetwoparallelanddistributedmodelsforinternalCVIsnamelyforSilhouetteandDunnindexes usingMapReduceframeworkunderHadoop.TheproposedmodelstermedasMR_Silhouetteand MR_Dunnhavebeentestedtosolveboththeissueofevaluatingtheclusteringresultsandidentifying theoptimalnumberofclusters.Theresultsofexperimentalstudyareverypromisingandshowthat theproposedparallelanddistributedmodelsachievetheexpectedtaskssuccessfully. KeywoRDS Big Data, Clustering, Data Mining, Dunn Index, Hadoop, Internal Clustering Validation Indexes, MapReduce, Optimal Number Of Clusters, Silhouette Index","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Models for Internal Clustering Validation Indexes Based on Hadoop-MapReduce\",\"authors\":\"Soumeya Zerabi, S. Meshoul, Samia Boucherkha\",\"doi\":\"10.4018/ijdst.2020070103\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Clustervalidationaimstobothevaluatetheresultsofclusteringalgorithmsandpredictthenumberof clusters.Itisusuallyachievedusingseveralindexes.Traditionalinternalclusteringvalidationindexes (CVIs)aremainlybasedincomputingpairwisedistanceswhichresultsinaquadraticcomplexity oftherelatedalgorithms.TheexistingCVIscannothandlelargedatasetsproperlyandneedtobe revisitedtotakeaccountoftheever-increasingdatasetvolume.Therefore,designofparalleland distributedsolutionstoimplementtheseindexesisrequired.Tocopewiththisissue,theauthors proposetwoparallelanddistributedmodelsforinternalCVIsnamelyforSilhouetteandDunnindexes usingMapReduceframeworkunderHadoop.TheproposedmodelstermedasMR_Silhouetteand MR_Dunnhavebeentestedtosolveboththeissueofevaluatingtheclusteringresultsandidentifying theoptimalnumberofclusters.Theresultsofexperimentalstudyareverypromisingandshowthat theproposedparallelanddistributedmodelsachievetheexpectedtaskssuccessfully. KeywoRDS Big Data, Clustering, Data Mining, Dunn Index, Hadoop, Internal Clustering Validation Indexes, MapReduce, Optimal Number Of Clusters, Silhouette Index\",\"PeriodicalId\":118536,\"journal\":{\"name\":\"Int. J. Distributed Syst. Technol.\",\"volume\":\"68 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Distributed Syst. Technol.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijdst.2020070103\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Distributed Syst. Technol.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijdst.2020070103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Models for Internal Clustering Validation Indexes Based on Hadoop-MapReduce
Clustervalidationaimstobothevaluatetheresultsofclusteringalgorithmsandpredictthenumberof clusters.Itisusuallyachievedusingseveralindexes.Traditionalinternalclusteringvalidationindexes (CVIs)aremainlybasedincomputingpairwisedistanceswhichresultsinaquadraticcomplexity oftherelatedalgorithms.TheexistingCVIscannothandlelargedatasetsproperlyandneedtobe revisitedtotakeaccountoftheever-increasingdatasetvolume.Therefore,designofparalleland distributedsolutionstoimplementtheseindexesisrequired.Tocopewiththisissue,theauthors proposetwoparallelanddistributedmodelsforinternalCVIsnamelyforSilhouetteandDunnindexes usingMapReduceframeworkunderHadoop.TheproposedmodelstermedasMR_Silhouetteand MR_Dunnhavebeentestedtosolveboththeissueofevaluatingtheclusteringresultsandidentifying theoptimalnumberofclusters.Theresultsofexperimentalstudyareverypromisingandshowthat theproposedparallelanddistributedmodelsachievetheexpectedtaskssuccessfully. KeywoRDS Big Data, Clustering, Data Mining, Dunn Index, Hadoop, Internal Clustering Validation Indexes, MapReduce, Optimal Number Of Clusters, Silhouette Index
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
