Title: A time- and memory-efficient frequent itemset discovering algorithm for association rule mining
Authors: Renata Ivancsy, Istvan Vajk
Addresses: Department of Automation and Applied Informatics, Budapest University of Technology and Economics and HAS-BUTE Control Research Group, 3 Goldmann Gy. ter, H 1111, Budapest, Hungary. ' Department of Automation and Applied Informatics, Budapest University of Technology and Economics and HAS-BUTE Control Research Group, 3 Goldmann Gy. ter, H 1111, Budapest, Hungary
Abstract: Frequent itemset discovering is a highly researched area in the field of data mining. The algorithms dealing with this problem have several advantages and disadvantages regarding their time complexity, I/O cost and memory requirement. There are algorithms that have moderate memory usage but high I/O cost, thus the execution time of them is high; such methods are for example the level-wise algorithms. Other methods have advantageous time behaviour; however, they are memory intensive, like the two-phase algorithms. In this paper, a novel algorithm, which is efficient both in time and memory, is proposed. The new algorithm discovers the small frequent itemsets quickly by taking advantage of the easy indexing opportunity of the suggested candidate storage structure. The main benefit of the novel algorithm is its advantageous time behaviour when using different types of datasets as well as its low I/O activity and moderate memory requirement.
Keywords: association rule mining; frequent itemsets; apriori algorithms; FP-growth algorithms; data mining.
International Journal of Computer Applications in Technology, 2006 Vol.27 No.4, pp.270 - 280
Published online: 08 Jan 2007 *Full-text access for editors Access for subscribers Purchase this article Comment on this article