Preprint · SSRN Electronic Journal · January 1, 2013

Enhancement of Intersecting Algorithm Using Prefix Tree for Transactions in Identification of Closed Frequent Item Sets in Data Mining

Veenita Gupta, Neeraj Kumar, Praveen Kumar

Abstract

Mining frequent itemsets is a fundamental task in data mining. Unfortunately the number of frequent itemsets describing the data is often too large to comprehend. This problem has been attacked by condensed representations of frequent itemsets that are subcollections of frequent itemsets containing only the frequent itemsets that cannot be deduced from other frequent itemsets in the subcollection, using some deduction rules. Most known frequent item set mining approaches enumerate candidate item sets, determine their support, and prune candidates that fail to reach the user-specified minimum support. Apart from this scheme we can use intersection approach for identifying frequent item set. The closed frequentitem sets can be represented as theintersection of some subset of the given transactions.As the transactional database increases, the size of prefix tree also grows which make it difficult to handle. Experiments have been done to find out the efficient memory utilization of prefix tree. An improvement has been suggested to reduce the total number of branches in the prefix tree leading to reduction in its size.

Data Mining Algorithms and ApplicationsAdvanced Database Systems and QueriesData Mining and Machine Learning Applicationsclosed frequent itemset miningprefix treetransactional databases

Enhancement of Intersecting Algorithm Using Prefix Tree for Transactions in Identification of Closed Frequent Item Sets in Data Mining

Abstract

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