Towards Efficient Mining of Periodic High-Utility Itemsets in Large Databases
P. Lalitha Kumari1, S. G. Sanjeevi2, T.V Madhusudhana Rao3
1P. Lalitha Kumari, Department of Computer Science and Engineering, National Institute of Technology, Warangal Dist, Telangana, India.
2S. G. Sanjeevi, Department of Computer Science and Engineering, National Institute of Technology, Warangal Dist, Telangana, India.
3T.V Madhusudhana Rao, Department of Computer Science and Engineering, Sri Sivani College of Engineering , Srikakulam, (Andhra Pradesh) India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on 30 November, 2019. | PP: 8083-8093 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8445118419/2019©BEIESP | DOI: 10.35940/ijrte.D8445.118419
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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 Item sets mining has attracted many researchers in recent years. But HUI mining methods involves a exponential mining space and returns a very large number of high-utility itemsets. . Temporal periodicity of itemset is considered recently as an important interesting criteria for mining high-utility itemsets in many applications. Periodic High Utility item sets mining methods has a limitation that it does not consider frequency and not suitable for large databases. To address this problem, we have proposed two efficient algorithms named FPHUI( mining periodic frequent HUIs), MFPHM(efficient mining periodic frequent HUIs) for mining periodic frequent high-utility itemsets. The first algorithm FPHUI miner generates all periodic frequent itemsets. Mining periodic frequent high-utility itemsets leads to more computational cost in very large databases. We further developed another algorithm called MFPHM to overcome this limitation. The performance of the frequent FPHUI miner is evaluated by conducting experiments on various real datasets. Experimental results show that proposed algorithms is efficient and effective.
Keywords: Periodic Occurrence, Frequent Periodic Itemsets, High Utility Itemsets, Large Transactional Databases.
Scope of the Article: Frequency Selective Surface.