International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Science , Computer Science and Engineering

Volume4,September 2017,

Topic : FiDoop-DP: An Efficient Data Mining Technique on Heterogeneous Clusters

Authors:Juturu Chandana ||

Abstract:Data mining is the analysis step of the “Knowledge discovery in data bases process” [1]. Actually, it is very hard to mine item-sets which are frequently used in the transactions. To identify frequently used item-sets, parallel algorithms which are used for mining were developed. These parallel algorithms were developed to balance the data and to maintain equal partitions of data, among a group of nodes which are to be computed. Because of redundant transactions, there is a significant performance problem of parallel frequent item-sets mining. Therefore, a data partitioning technique has been developed. FiDoop-DP is a kind of data partitioning method which is used to divide the data based on item-sets of the transaction which are brought by the clients or customers. To know better about frequent item-sets i.e., products which are regularly sold together, an algorithm is used for time consumption while running data which is extremely large. This algorithm is named as Equivalence Class clustering and Lattice Traversal algorithm (ECLAT). This ECLAT algorithm is combined with the Map-Reduce functionality, and then it gives better solutions within small amount of time. At the same time ECLAT is combined with Local sensitive hashing technique for better performance of items which are present at locally present in the data nodes. By combining those two techniques, the performance of FiDoop increases. This is known by the time taken to mine frequent item-sets. The main goal of this paper is to mine the itemsets which are prominently used or sold in the market by that it can increase the sales of those products..

Keywords: Frequent Item-set Mining, Data Partitioning, ECLAT algorithm, Time management, Map-Reduce, Hadoop Cluster

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DOI: 01.1617/vol4/iss9/pid68523

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Dr. Allon Guez
Professor, Drexel University,

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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Journal Code : IJERCSE
Electronic ISSN : 2394-2320
Impact Factor : 4.890
Frequency : monthly
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