The mining of association rules is one of the database mining techniques used to extract useful information from large quantities of data. Finding association rules, however, requires that the transaction database be scanned repeatedly, and we need to handle very large amounts of transaction data. This requires an incredibly large amount of computation time. There have therefore been many attempts to speed-up database mining by using parallel computers. Recent improvements in the performance of PCs and workstations (WSs), and the recent dissemination of network technology have made parallel processing distributed within computer clusters an attractive alternative to parallel computers. In this paper we describe two new algorithms effective for parallel processing distributed in a WS cluster environment. One is an algorithm in which the size of data transmitted between WSs is smaller than that of the former algorithm. The other is an algorithm that reduces the amount of scan processing at each node by dividing data and that uses shift operations for data communication. We have implemented these algorithms on a WS cluster and have evaluated their performance.
['Tomohiro Shimomura', 'Susumu Shibusawa']
Performance evaluation of distributed algorithms for mining association rules on workstation cluster