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Title: Clustering transactions using large items
Authors: Wang, Ke
Xu, Chu
Liu, Bing 
Issue Date: 1999
Citation: Wang, Ke,Xu, Chu,Liu, Bing (1999). Clustering transactions using large items. International Conference on Information and Knowledge Management, Proceedings : 483-490. ScholarBank@NUS Repository.
Abstract: 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 providing a better solution to the clustering problem. We present a clustering algorithm based on the new clustering criterion and evaluate its effectiveness.
Source Title: International Conference on Information and Knowledge Management, Proceedings
ISBN: 1581131461
Appears in Collections:Staff Publications

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