A Localized Algorithm for Parallel Association Mining

Discovery of association rules is an important database mining problem. Mining for association rules involves extracting patterns from large databases and inferring useful rules from them. Several parallel and sequential algorithms have been proposed in the literature to solve this problem. Almost all of these algorithms make repeated passes over the database to determine the commonly occurring patterns or itemsets (set of items), thus incurring high I/O overhead. In the parallel case, these algorithms do a reduction at the end of each pass to construct the global patterns, thus incurring high synchronization cost. In this paper we describe a new parallel association mining algorithm. Our algorithm is a result of detailed study of the available parallelism and the properties of associations. The algorithm uses a scheme to cluster related frequent itemsets together, and to partition them among the processors. At the same time it also usesa different databaselayout which clusters relate...

• Type: Conference
• Year: 1997
• Where: SPAA
• Authors: Mohammed Javeed Zaki, Srinivasan Parthasarathy, Wei Li
• Keywords: Algorithm, Association Mining Algorithm, Association Rules, Distributed And Parallel Computing, SPAA 1997