Mining Frequent Pattern from Large Dynamic Database Using Compacting Data Sets
Keywords:
Compacting Frequent Pattern Candidate Item sets, Intersection, Duplicate, UnneededAbstract
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.
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