An efficient clustering algorithm for market basket data based on small large ratios

C. H. Yun; Kun-Ta Chuang; M. S. Chen

An efficient clustering algorithm for market basket data based on small large ratios

C. H. Yun, Kun-Ta Chuang, M. S. Chen

Department of Computer Science and Information Engineering

Research output: Contribution to journal › Conference article › peer-review

33 Citations (Scopus)

Abstract

In this paper, we devise an efficient algorithm for clustering market-basket data items. In view of the nature of clustering market basket data, we devise in this paper a novel measurement, called the small-large (abbreviated as SL) ratio, and utilize this ratio to perform the clustering. With this SL ratio measurement, we develop an efficient clustering algorithm for data items to minimize the SL ratio in each group. The proposed algorithm not only incurs an execution time that is significantly smaller than that by prior work but also leads to the clustering results of very good quality.

Original language	English
Pages (from-to)	505-510
Number of pages	6
Journal	Proceedings - IEEE Computer Society's International Computer Software and Applications Conference
Publication status	Published - 2001 Jan 1
Event	25th Annual International Computer Software and Applications Conference (COMPSAC)2001 - Chicago, IL, United States Duration: 2001 Oct 8 → 2001 Oct 12

All Science Journal Classification (ASJC) codes

Software
Computer Science Applications

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abstract = "In this paper, we devise an efficient algorithm for clustering market-basket data items. In view of the nature of clustering market basket data, we devise in this paper a novel measurement, called the small-large (abbreviated as SL) ratio, and utilize this ratio to perform the clustering. With this SL ratio measurement, we develop an efficient clustering algorithm for data items to minimize the SL ratio in each group. The proposed algorithm not only incurs an execution time that is significantly smaller than that by prior work but also leads to the clustering results of very good quality.",

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AU - Chuang, Kun-Ta

AU - Chen, M. S.

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N2 - In this paper, we devise an efficient algorithm for clustering market-basket data items. In view of the nature of clustering market basket data, we devise in this paper a novel measurement, called the small-large (abbreviated as SL) ratio, and utilize this ratio to perform the clustering. With this SL ratio measurement, we develop an efficient clustering algorithm for data items to minimize the SL ratio in each group. The proposed algorithm not only incurs an execution time that is significantly smaller than that by prior work but also leads to the clustering results of very good quality.

AB - In this paper, we devise an efficient algorithm for clustering market-basket data items. In view of the nature of clustering market basket data, we devise in this paper a novel measurement, called the small-large (abbreviated as SL) ratio, and utilize this ratio to perform the clustering. With this SL ratio measurement, we develop an efficient clustering algorithm for data items to minimize the SL ratio in each group. The proposed algorithm not only incurs an execution time that is significantly smaller than that by prior work but also leads to the clustering results of very good quality.

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T2 - 25th Annual International Computer Software and Applications Conference (COMPSAC)2001

Y2 - 8 October 2001 through 12 October 2001

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