An Artificial Bee Colony Algorithm to Mine Periodic High Utility Itemsets
S. Viveka1, B. Kalaavathi2
1S. Viveka, Assistant Professor, Department of Information Technology, Velalar College of Engineering and Technology, Erode (Tamil Nadu), India.
2Dr. B.Kalaavathi, Professor & Head, Department of Computer Science, K.S.R. Institute for Engineering and Technology, Tiruchengode (Tamil Nadu), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 1386-1395 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3921048619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: High utility itemset mining is one of the key research areas in data mining in recent years. The main challenge in high utility itemset mining is the exponential growth space for finding the high utility itemsets. Several algorithms were proposed to mine high utility itemsets and all of them suffer because of the huge search space. Genetic Algorithms (GA) are now attracting researchers since it reduces the search space tremendously. In this paper, a novel algorithm based on Artificial Bee Colony PHUIM-ABC is proposed to mine the periodic high utility itemsets. During initial phase, the transaction database is scanned to find the 1-High Transaction Weighted Utility Itemsets (1-HTWUI). The 1-HTWUI is used as measured as the parameter in assigning the onlooker bee. Five real life data sets are used to evaluate the performance of the proposed algorithm with the existing state-of-art algorithms .The execution time and memory usage are the parameters used to measure the performance. Experimental results show that the proposed PHUIM-ABC algorithm performs better than the state-of-art non heuristic algorithms.
Keyword: Artifical bee Colony, Genetic Algorithm, High Utility Itemset, Itemset Mining, Periodic High Utility Itemset, Utility Mining.
Scope of the Article: Artificial Intelligence and Machine Learning