Author(s)

F. Wang, N. Helian & Y. J. Yip

Abstract

Association rule mining using list representation F. wang', N. ~elian' & Y. J. yip2 I School of Computing, Communication and Mathematics, London Metropolitan University, UK 2 School of Computing and Mathematics, University of Teesside, UK Abstract Typically 80% of the data in the logical OLAP datacube, the core engine of data warehouses, are zero. When it comes to sparse, the performance quickly degrades due to the heavy VO overheads in sorting and merging intermediate results. In this work, we first introduce a list representation in main memory for storing and computing datasets. The sparse transaction dataset is compressed as the empty cells are removed Accordingly we propose a new algorithm for association rule mining on the platform of list representation, which just needs to scan the transaction database once to generate all the possible rules. In contrast, the well-known a priori algorithm requires repeated scans of the databases, thereby resulting in heavy VO accesses particularly when

Keywords