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dc.contributor.authorChen, Lifeizh_CN
dc.contributor.authorHe, Shanjunzh_CN
dc.contributor.authorJiang, Qingshanzh_CN
dc.contributor.author姜青山zh_CN
dc.date.accessioned2015-07-22T07:10:28Z
dc.date.available2015-07-22T07:10:28Z
dc.date.issued2009-12zh_CN
dc.identifier.citationFRONTIERS OF COMPUTER SCIENCE IN CHINA, 2009,3(4):477-484zh_CN
dc.identifier.otherWOS:000207971200006zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/90340
dc.descriptionNational Natural Science Foundation of China [10771176]; National Science Foundation of Fujian Province of China [2009J01273]zh_CN
dc.description.abstractCluster validation is a major issue in cluster analysis of data mining, which is the process of evaluating performance of clustering algorithms under varying input conditions. Many existing validity indices address clustering results of low-dimensional data. Within high-dimensional data, many of the dimensions are irrelevant, and the clusters usually only exist in some projected subspaces spanned by different combinations of dimensions. This paper presents a solution to the problem of cluster validation for projective clustering. We propose two new measurements for the intracluster compactness and intercluster separation of projected clusters. Based on these measurements and the conventional indices, three new cluster validity indices are presented. Combined with a fuzzy projective clustering algorithm, the new indices are used to determine the number of projected clusters in high-dimensional data. The suitability of our proposal has been demonstrated through an empirical study using synthetic and real-world datasets.zh_CN
dc.language.isoen_USzh_CN
dc.publisherFRONT COMPUT SCI CHIzh_CN
dc.source.urihttp://dx.doi.org/10.1007/s11704-009-0051-1zh_CN
dc.titleValidation indices for projective clusteringzh_CN
dc.typeArticlezh_CN


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