A novel approach for mining maximal frequent patterns |
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| Affiliation: | 1. Faculty of Information Technology, Ho Chi Minh City University of Technology, Vietnam;2. College of Electronics and Information Engineering, Sejong University, Seoul, Republic of Korea;3. University of Economics & Finance, Ho Chi Minh City, Vietnam;4. Division of Data Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam;5. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam;6. Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China;1. School of Information Technology & Mathematical Sciences, University of South Australia, SA 5001, Australia;2. School of Engineering, University of South Australia, SA 5001, Australia;3. Respiratory & Sleep Medicine, Women''s and Children''s Hospital, North Adelaide, SA 5006, Australia;4. Robinson Research Institute, Adelaide Medical School, University of Adelaide, SA 5005, Australia;1. LIRMM - 860, rue de Saint Priest, Montpellier, 34095, France;2. INRA - UMR IATE - 2, place Pierre Viala, Montpellier, 34060, France;3. UMR MIA-Paris, AgroParisTech, INRA, Université Paris-Saclay, Paris,75005, France;4. CIRAD - UMR TETIS - 500, rue J.F. Breton, Montpellier 34093, France;1. Faculty of Information Technology, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam;2. College of Electronics and Information Engineering, Sejong University, Seoul, Republic of Korea;3. Division of Data Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam;4. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam;5. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6R 2V4 AB, Canada;6. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia;7. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland |
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| Abstract: | ![]()
Mining maximal frequent patterns (MFPs) is an approach that limits the number of frequent patterns (FPs) to help intelligent systems operate efficiently. Many approaches have been proposed for mining MFPs, but the complexity of the problem is enormous. Therefore, the run time and memory usage are still large. Recently, the N-list structure has been proposed and verified to be very effective for mining FPs, frequent closed patterns, and top-rank-k FPs. Therefore, this paper uses the N-list structure for mining MFPs. A pruning technique is also proposed to prune branches to reduce the search space. This technique is applied to an algorithm called INLA-MFP (improved N-list-based algorithm for mining maximal frequent patterns) for mining MFPs. Experiments were conducted to evaluate the effectiveness of the proposed algorithm. The experimental results show that INLA-MFP outperforms two state-of-the-art algorithms for mining MFPs. |
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