Efficient Algorithm using Big Data for Frequent Itemsets Mining
Srinivasa Rao Divvela1, V Sucharitha2

1Divvela Srinivasa Rao, Sr. Assistant Professor, Laki Reddy Balireddy College of Engineering, Mylavaram, (A.P), India.
2Dr. V. Sucharita, Professor, Department of Computer Science and Engineering, Narayana Engineering College, Gudur (A.P), India.
Manuscript received on 05 February 2019 | Revised Manuscript received on 13 February 2019 | Manuscript published on 28 February 2019 | PP: 394-396 | Volume-8 Issue-4, February 2019 | Retrieval Number: D2698028419/19©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: Future trends are being estimated with the help of tools in data mining which allows the making of decisions to be data driven and analyze them carefully with the corresponding tools. In various fields of mining the most important practice of mining of data is the Associate-rule mining. Major issue in any of the techniques being the generation of the frequent data-item sets which has to be solved efficiently. Many techniques have been put forth for this only purpose of itemset generation like Apriori-algorithm, FP_Growth-algorithm, and many other solutions are being offered to solve the issue. Many outsets of the problem yet to be fully implemented such as large clusters solving and distribution along with parallelization (automatic) etc. Many of these issues can be solved with the implementation of Framework of MapReduce on Improved Apriori algorithm. Lessening of time due to parallel executions can be achieved with the help of this. This procedure considerably decreases the time of execution and also a significant rise in efficiency is observed.
Keyword: Map Reduce, Improved Apriori, Mining, Frequent Data-item Sets.
Scope of the Article: Big Data Networking