Frequent itemset mining using cellular learning automata |
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| Affiliation: | 1. Department for Management of Science and Technology Development & Faculty of Social Sciences and Humanities, Ton Duc Thang University, Ho Chi Minh City, Vietnam;2. Department of Teacher Education, University of Helsinki, Finland;3. Department of Industrial Engineering and Management, School of Science, Aalto University, Finland;4. Optentia Research Focus Area, North-West University, Vanderbijlpark, South Africa;5. Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taiwan;1. Weber State University, USA;2. University of Oklahoma, USA;3. Human Resources Research Organization, USA;4. American Institutes for Research, USA;5. University of California Santa Barbara, USA;1. Department of Information Management, Chang Gung University, Taiwan;2. Graduate Institute of Business and Management, Chang Gung University, Taiwan;3. Department of Industrial and Business Management, Chang Gung University, Taiwan;4. Department of Rehabilitation, Chang Gung Memorial Hospital, Taiwan;5. Department of Business and Management, Ming Chi University of Technology, Taiwan;1. Saarland University, Germany;2. University of Luxembourg, Luxembourg;3. Universität Regensburg, Germany;1. Clinic for Neuropsychiatry, School of Medicine, University of Priština/Kosovska Mitrovica, Anri Dinana bb, Priština/Kosovska Mitrovica, Serbia;2. MSc Quantitive Finance, ETH Zurich & University of Zurich, Switzerland;1. School of Media and Communication, #1411, bldg. 303, Heukseok-ro 84, Dongjak-gu, Seoul, 06974, South Korea;2. Department of Interaction Science, Sungkyunkwan University, Seoul, South Korea |
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| 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. |
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| Keywords: | Frequent itemset mining Cellular automata Data mining Association rules Parallel frequent itemset mining |
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