Novel Utility Procedure for Filtering High Associated Utility Items from Transactional Databases
Srihari Varma Mantena1, CVPR Prasad2
1Srihari Varma Mantena, Research Scholar, Dept of CSE, Acharya Nagarjuna University, Guntur-522510, AP, India.
2Dr CVPR Prasad, Research Supervisor, Dept of CSE, Acharya Nagarjuna University, Guntur-522510, AP, India.
Manuscript received on August 03, 2019. | Revised Manuscript received on August 28, 2019. | Manuscript published on August 30, 2019. | PP: 2961-2966 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8730088619/2019©BEIESP | DOI: 10.35940/ijeat.F8730.088619
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Abstract: In data mining, mining and analysis of data from different transactional data sources is an aggressive concept to explore optimal relations between different item sets. In recent years number of algorithms/methods was proposed to mine associated rule based item sets from transactional databases. Mining optimized high utility (like profit) association rule based item sets from transactional databases is still a challenging task in item set extraction in terms of execution time. We propose High Utility based Association Pattern Growth (HUAPG) approach to explore high association utility item sets from transactional data sets based on user item sets. User related item sets to mine associated items using utility data structure (UP-tree) with respect to identification of item sets in proposed approach. Proposed approach performance with compared to hybrid and existing methods worked on synthetic related data sets. Experimental results of proposed approach not only filter candidate item sets and also reduce the run time when database contain high amount of data transactions.
Keywords: Transactional databases, utility pattern, Association, high utility, utility pattern tree, item sets and data mining.