Title :
An efficient clustering algorithm for market basket data based on small large ratios
Author :
Yun, Ching-Huang ; Chuang, Kun-Ta ; Chen, Ming-Syan
Author_Institution :
Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan
fDate :
6/23/1905 12:00:00 AM
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
Keywords :
data mining; pattern clustering; SL ratio measurement; clustering algorithm; clustering analysis; data mining; execution time; market basket data; small large ratios; Association rules; Clustering algorithms; Costs; Damping; Data analysis; Data mining; Marine vehicles; Transaction databases;
Conference_Titel :
Computer Software and Applications Conference, 2001. COMPSAC 2001. 25th Annual International
Conference_Location :
Chicago, IL
Print_ISBN :
0-7695-1372-7
DOI :
10.1109/CMPSAC.2001.960660