期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：23
出版社：Journal of Theoretical and Applied
摘要：Association rule mining is a very efficient technique for finding a strong relation between correlated data. For the mining of positive and negative rules, a variety of algorithms are used such as Apriori algorithm. Practically the data set can contain a large number of data entities which form a large number of rules set. For the understanding of efficient relation, it is needed that rules should be kept as minimum as possible without losing the useful information. This is a difficult task and can be seen as optimization problem. To eliminate the multi-scan problem and reduce a large number of mined rules, in this paper, we propose a novel approach to mine interesting positive and negative rules from frequent and infrequent pattern set based on genetic Tabu heuristic search with key innovation to optimize genetic population of rules. Genetic Tabu Algorithm (GTA) follows procedures similar to the Genetic Algorithm (GA). However, for the selection process, GTA uses the Tabu search process to enhance the quality of mined rules in some way that is efficient in time and memory. The suggested algorithm is accomplished in two phases: (1) generate frequent and infrequent pattern set. (2) Efficiently generate optimized positive and negative rules by using useful frequent pattern set and GTA. Experiment results show that the proposed algorithm can efficiently generate positive and negative association rules and outperforms the traditional algorithms in terms of time and quality.
