Optimization Of Intersecting Algorithm For Transactions Of Closed Frequent Item Sets In Data Mining

Data mining is the computer-assisted process of information analysis. 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 sub collections 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 enumerates 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. Analysis and experiments have been done to find out memory utilization of closed frequent itemset in prefix tree. An enhancement has been suggested to reduce the total number of branches in the prefix tree leading to reduce in its size. Keyword:Intersecting Algorithm Frequent itemset , prefix tree