An efficient cluster and decomposition algorithm for mining association rules

in Sciences Citation Index(SCI), 科學引文索引資料庫(SCI)
標題An efficient cluster and decomposition algorithm for mining association rules
出版類型SCI(Sciences Citation Index)
出版年度2004
AuthorsYuh-Jiuan Tsay, 蔡玉娟
開始頁161
頁數10
出版日期2004 / 3
其他編號0000
中文摘要

Conventional algorithms for mining association rules operate in a combination of smaller large itemsets. This paper presents a new efficient which combines both the cluster concept and decomposition of larger candidate itemsets, while proceeds from mining the maximal large itemsets down to large 1-itemsets, named cluster-decomposition association rule (CDAR). First, the CDAR method creates some clusters by reading the database only once, and then clustering the transaction records to the kth cluster, where the length of a record is k. Then, the large k-itemsets are generated by contrasts with the kth cluster only, unlike the combination concept that contrasts with the entire database. Experiments with real-life databases show that CDAR outperforms Apriori, a well-known and widely used association rule.

網址http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V0C-49JX0XK-5&_user=2535637&_coverDate=03%2F22%2F2004&_alid=1250878933&_rdoc=1&_fmt=high&_orig=search&_cdi=5643&_sort=r&_docanchor=&view=c&_ct=532&a
期刊名稱Information Sciences
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