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 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Science Applications
Cite this
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An efficient clustering algorithm for market basket data based on small large ratios. / Yun, C. H.; Chuang, Kun-Ta; Chen, M. S.
In: Proceedings - IEEE Computer Society's International Computer Software and Applications Conference, 01.01.2001, p. 505-510.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - An efficient clustering algorithm for market basket data based on small large ratios
AU - Yun, C. H.
AU - Chuang, Kun-Ta
AU - Chen, M. S.
PY - 2001/1/1
Y1 - 2001/1/1
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.
UR - http://www.scopus.com/inward/record.url?scp=0035169515&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0035169515&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:0035169515
SP - 505
EP - 510
JO - Proceedings - IEEE Computer Society's International Computer Software and Applications Conference
JF - Proceedings - IEEE Computer Society's International Computer Software and Applications Conference
SN - 0730-6512
ER -