Authors: Mohamed H. Marghny; Hosam E. Refaat
Addresses: Department of Computer Science, Faculty of Computers and Information, Assiut University, Assiut 71615, Egypt ' Department of Computer Science, Faculty of Computers and Information, Assiut University, Assiut 71615, Egypt
Abstract: Frequent itemset finding is the most time consuming step in analysing large transactional databases. The use of sequential algorithms cannot give analytical ability for such very large databases especially in terms of run-time performance. Therefore, we must rely on high performance parallel computing. In this paper, we present a new parallel algorithm for frequent itemset mining, called 'HorVertical' algorithm. This algorithm introduces a new database partitioning called 'HorVertical' partitioning. This technique in partitioning the database reduces the dependency in the parallel computation and gives new properties to reduce the computations. The algorithm passes the database only one time and starts a new stage with the finished itemsets while some other itemsets in the same stage have not been finished yet. We present the result on the performance of our algorithm on various databases, and compare it against well known algorithms.
Keywords: parallel algorithms; distributed shared memory; association rule mining; Linda system; HorVertical partitioning; parallel computing; itemset mining; database partitioning.
International Journal of Business Intelligence and Data Mining, 2012 Vol.7 No.4, pp.233 - 252
Available online: 27 Jan 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article