Mining of Frequent Closed Itemsets: A Review

Computer Engineering

Our capabilities of both generating and collecting data have been increasing rapidly. Mining frequent patterns is one of the most important concepts of data mining. Several algorithms have been developed for finding frequent item sets from the databases. The efficiency of these algorithms is a major issue since a long time. In data mining, association rule mining is one of the important techniques for discovering meaningful patterns from large collection of data. Discovering frequent item sets play an important role in mining association rules, sequence rules, web log mining and many other interesting patterns among complex data. The problem of mining association rules has attracted lots of attention in the research community. The most time consuming operation in discovering association rule, is the computation of the frequency of the occurrences of interesting subset of items (called candidates) in the database of transactions. Existing Apriori, FP Growth and Closet+ used with aim of improving the performances of high utility item sets. Main aim of this review is developing an efficient algorithm for finding frequent closed patterns. In this review paper we review some existing algorithms for frequent closed item set mining. The present paper provides an overview of various research papers on frequent closed itemset mining algorithms.

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