Mining Frequent Pattern from Large Dynamic Database Using Compacting Data Sets

Nidhi Sethi¹ , Pradeep Sharma²

Section:Research Paper, Product Type: Isroset-Journal
Vol.1 , Issue.3 , pp.31-34, May-2013

Online published on Jul 07, 2013

Copyright © Nidhi Sethi , Pradeep Sharma . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Frequent pattern mining has been a focused theme in data mining research and the first step in the analysis of data rising in a broad range of applications. Apriori based algorithms have used candidate itemsets generation method, but this approach was highly time-consuming. Several research works have been carried out which can avoid the generating vast volume of candidate itemsets. In this paper, a new approach Compacting Data Sets is introduced. In Compacting Data Sets (CDS) approach first merging of duplicate transactions is being performed and then intersection between itemsets is taken and then deleting unneeded subsets repeatedly. This algorithm differs from all classical frequent itemset discovering algorithms in such a way that it not only removes unnecessary candidate generation but also removes duplicate transactions.

Key-Words / Index Term :
Compacting Frequent Pattern Candidate Item sets, Intersection, Duplicate, Unneeded

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