Performance Analysis of Data Mining Algorithms to Generate Frequent Itemset

Gigantic amount of data records i.e. in terabytes or more are available in science, industry, business and many other areas. Such data can provide rich information of strategic importance which can be utilized. But the problem is how to obtain this information. Today the answer is data mining! Now a day the frequent itemset mining has became one of the hottest research topics in the field of data mining. The past decades has witnessed that hundereds of research papers have been published for presenting new or improvements in the existing frequent itemset mining algorithms. In this paper the frequent itmset mining algoritms i.e. Apriori, FP-Growth, ECLAT, and RELIM are presented with their theoretical and experimental analysis