Frequent itemset mining using cellular learning automata. (March 2017)

• Record Type: Journal Article
• Authors: Sohrabi, Mohammad Karim; Roshani, Reza
• Is Part Of: Computers in human behavior. Volume 68(2017)
• Page Start: 244
• Page End: 253
• Publication Date: 2017-03
• Subjects: Frequent itemset mining -- Cellular automata -- Data mining -- Association rules -- Parallel frequent itemset mining; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2016.11.036
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: A core issue of the association rule extracting process in the data mining field is to find the frequent patterns in the database of operational transactions. If these patterns discovered, the decision making process and determining strategies in organizations will be accomplished with greater precision. Frequent pattern is a pattern seen in a significant number of transactions. Due to the properties of these data models which are unlimited and high-speed production, these data could not be stored in memory and for this reason it is necessary to develop techniques that enable them to be processed online and find repetitive patterns. Several mining methods have been proposed in the literature which attempt to efficiently extract a complete or a closed set of different types of frequent patterns from a dataset. In this paper, a method underpinned upon Cellular Learning Automata (CLA) is presented for mining frequent itemsets. The proposed method is compared with Apriori, FP-Growth and BitTable methods and it is ultimately concluded that the frequent itemset mining could be achieved in less running time. The experiments are conducted on several experimental data sets with different amounts of minsup for all the algorithms as well as the presented method individually. Eventually the results prod to the effectiveness of the proposed method. Highlights: In this paper, a frequent itemset mining method is presented. We use cellular learning automata to do fast parallel mining process. The proposed algorithm was tested and the results were compared to SABMA. Experiments are conducted on several experimental data sets with different minsups. Performance study shows that our algorithm outperforms the best former algorithms.