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1999
Springer

Clustering Transactions Using Large Items

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Clustering Transactions Using Large Items
In 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 providinga better solution tothe clustering problem. We present a clustering algorithm based on the new clustering criterion and evaluate its e ectiveness.
Ke Wang, Chu Xu, Bing Liu
Added 03 Aug 2010
Updated 03 Aug 2010
Type Conference
Year 1999
Where CIKM
Authors Ke Wang, Chu Xu, Bing Liu
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