Data mining is a classical research area. That is utilized for analyzing data for prepare a model which will able to deal with the classification problems, categorization problems and rule building techniques. The rule building techniques are basically utilized to understand or map the attribute relationships for making smart decisions. Among different rule mining techniques association rule mining or frequent pattern mining technique is one of the popular techniques. In this presented work, the frequent pattern mining algorithm is the key area of investigation.Therefore first a review has been carried out for identifying the recent applications of frequent pattern mining algorithms. Next three popular frequent pattern mining algorithms are considered for experimental study. The experiments on publically available UCI based datasets has been carried out and the performance of the algorithms has been measured and compared with each other. According to the performance measurement we found that the éclat is efficient and effective algorithm for frequent pattern mining. But the apriori algorithm is most promising algorithm among the compared algorithms. Therefore, based on experiments the key problem has been identified and future extension of the work has been proposed.

Frequent pattern mining, Association rule mining, Data mining, Rule mining techniques, experimental comparison.