DocumentCode :
2089146
Title :
Mining association rules with weighted items
Author :
Cai, C.H. ; Fu, Ada W C ; Cheng, C.H. ; Kwong, W.W.
Author_Institution :
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong
fYear :
1998
fDate :
8-10 Jul 1998
Firstpage :
68
Lastpage :
77
Abstract :
Discovery of association rules has been found useful in many applications. In previous work, all items in a basket database are treated uniformly. We generalize this to the case where items are given weights to reflect their importance to the user. The weights may correspond to special promotions on some products, or the profitability of different items. We can mine the weighted association rules with weights. The downward closure property of the support measure in the unweighted case no longer exists and previous algorithms cannot be applied. In this paper, two new algorithms are introduced to handle this problem. In these algorithms we make use of a metric called the k-support bound in the mining process. Experimental results show the efficiency of the algorithms for large databases
Keywords :
database theory; deductive databases; knowledge acquisition; retail data processing; very large databases; association rule mining; basket database; data mining; downward closure property; k-support bound; large databases; profitability; retail database; rule discovery; weighted association rules; weighted items; Algorithm design and analysis; Application software; Association rules; Computer science; Data mining; Itemsets; Marketing management; Transaction databases;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Database Engineering and Applications Symposium, 1998. Proceedings. IDEAS'98. International
Conference_Location :
Cardiff
ISSN :
1098-8068
Print_ISBN :
0-8186-8307-4
Type :
conf
DOI :
10.1109/IDEAS.1998.694360
Filename :
694360
Link To Document :
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