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25th Annual International Computer Software and Applications Conference (COMPSAC'01)
An Efficient Clustering Algorithm for Market Basket Data Based on Small Large Ratios
Chicago, Illinois
October 08-October 12
ISBN: 0-7695-1372-7
ASCII Text
x 
 
Ching-Huang Yun, Kun-Ta Chuang, Ming-Syan Chen, "An Efficient Clustering Algorithm for Market Basket Data Based on Small Large Ratios," Computer Software and Applications Conference, Annual International, pp. 505, 25th Annual International Computer Software and Applications Conference (COMPSAC'01), 2001.
 
 
BibTex
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@article{ 10.1109/CMPSAC.2001.960660,
author = {Ching-Huang Yun and Kun-Ta Chuang and Ming-Syan Chen},
title = {An Efficient Clustering Algorithm for Market Basket Data Based on Small Large Ratios},
journal ={Computer Software and Applications Conference, Annual International},
volume = {0},
year = {2001},
issn = {0730-3157},
pages = {505},
doi = {http://doi.ieeecomputersociety.org/10.1109/CMPSAC.2001.960660},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer Software and Applications Conference, Annual International
TI - An Efficient Clustering Algorithm for Market Basket Data Based on Small Large Ratios
SN - 0730-3157
SP
EP
A1 - Ching-Huang Yun,
A1 - Kun-Ta Chuang,
A1 - Ming-Syan Chen,
PY - 2001
KW - Data mining
KW - clustering analysis
KW - market-basket data
KW - small-large ratios.
VL - 0
JA - Computer Software and Applications Conference, Annual International
ER -
 
Ching-Huang Yun, National Taiwan University
Kun-Ta Chuang, National Taiwan University
Ming-Syan Chen, National Taiwan University
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.
Index Terms:
Data mining, clustering analysis, market-basket data, small-large ratios.
Citation:
Ching-Huang Yun, Kun-Ta Chuang, Ming-Syan Chen, "An Efficient Clustering Algorithm for Market Basket Data Based on Small Large Ratios," compsac, pp.505, 25th Annual International Computer Software and Applications Conference (COMPSAC'01), 2001
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