Abstrait

ENHANCEMENT OF INTERSECTING ALGORITHM USING PREFIX TREE FOR TRANSACTIONS IN IDENTIFICATIONS OF CLOSED FREQUENT ITEM SETS IN DATA MINING

Veenita Gupta, Neeraj Kumar, Praveen Kumar

Mining frequent item sets is a fundamental task in data mining. Unfortunately the number of frequent item sets describing the data is often too large to comprehend. This problem has been attacked by condensed representations of frequent item sets that are sub collections of frequent item sets containing only the frequent item sets that cannot be deduced from other frequent item sets in the sub collection, 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 frequent item sets can be represented as the intersection 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