Implementing Frequent Itemset Mining By advanced Distributed Approach Using MatrixBased Pruning
Srinivasa Rao Divvela1, V Sucharita2
1Srinivasa Rao Divvela, Research Scholar Department of CSE KL University Lakireddy Bali Reddy College of Engineering Vaddeswaram (Andhra Pradesh), India.
2Dr V Sucharita, Professor Department of CSE Narayana Engineering College Gudur (Andhra Pradesh), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 2491-2493 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7542068519/19©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: Mining of frequent itemsets is a very crucial process in the mining of associate rules. Significant challenges are being encountered in this era where big data has drawn its own circle by shaping around space and time factors. A distributed procedure for the job is what that best be defined. So we increment our previous version with an enhanced version by the implementing a distributed approach which is better that FP growth. Differentiating of the existing and proposed algorithm is done using the practical valuable data that is available.
Keywords: Big Data, FP Growth, Distributed Approach, Frequent Itemset.
Scope of the Article: Big Data Analytic