Implementing Frequent Item set Mining by Overcoming Over-Scan Problems
Srinivasa Rao Divvela1, V Sucharita2
1Divvela Srinivasa Rao, 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 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 816-819 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6258048419/19©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: This paper over comes the problem of over-scan problems which are occurring frequently in the frequent itemset mining. In case of improved apriori we improve the efficiency and decrease the time lapse but it is unable to solve the over-scan problem efficiently. We now propose a systematic approach for the immediate solving of the over-scan problems by implementing the RP Tree on spark framework. The adaptation of this approach will be very useful in the formation of the tree of frequent patterns and also for the visualization of the frequent-1- itemsets. This is mainly to overcome the over-scan problems in the previous improved algorithm.
Keywords: Frequent Itemset Mining, RP Tree, Spark Framework, Visualization, Over-Scan Problems.
Scope of the Article: Mining Sciences