CBAR: An efficient method for mining association rules

in Sciences Citation Index(SCI), 科學引文索引資料庫(SCI)
標題CBAR: An efficient method for mining association rules
出版類型SCI(Sciences Citation Index)
AuthorsYuh-Jiuan Tsay, 蔡玉娟
出版日期2005 / 4

The discovery of association rules is an important data-mining task for which many algorithms have been proposed. However, the
efficiency of these algorithms needs to be improved to handle real-world large datasets. In this paper, we present an efficient algorithm named
cluster-based association rule (CBAR). The CBAR method is to create cluster tables by scanning the database once, and then clustering the
transaction records to the k-th cluster table, where the length of a record is k. Moreover, the large itemsets are generated by contrasts with the
partial cluster tables. This not only prunes considerable amounts of data reducing the time needed to perform data scans and requiring less
contrast, but also ensures the correctness of the mined results. Experiments with the FoodMart transaction database provided by Microsoft
SQL Server show that CBAR outperforms Apriori, a well-known and widely used association rule.
q 2004 Elsevier B.V. All rights reserved.

期刊名稱Knowledge-Based Systems
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