Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/39986
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dc.titleClustering transactions using large items
dc.contributor.authorWang, Ke
dc.contributor.authorXu, Chu
dc.contributor.authorLiu, Bing
dc.date.accessioned2013-07-04T07:54:05Z
dc.date.available2013-07-04T07:54:05Z
dc.date.issued1999
dc.identifier.citationWang, Ke,Xu, Chu,Liu, Bing (1999). Clustering transactions using large items. International Conference on Information and Knowledge Management, Proceedings : 483-490. ScholarBank@NUS Repository.
dc.identifier.isbn1581131461
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39986
dc.description.abstractIn traditional data clustering, similarity of a cluster of objects is measured by pairwise similarity of objects in that cluster. We argue that such measures are not appropriate for transactions that are sets of items. We propose the notion of large items, i.e., items contained in some minimum fraction of transactions in a cluster, to measure the similarity of a cluster of transactions. The intuition of our clustering criterion is that there should be many large items within a cluster and little overlapping of such items across clusters. We discuss the rationale behind our approach and its implication on providing a better solution to the clustering problem. We present a clustering algorithm based on the new clustering criterion and evaluate its effectiveness.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleInternational Conference on Information and Knowledge Management, Proceedings
dc.description.page483-490
dc.description.coden217
dc.identifier.isiutNOT_IN_WOS
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