A Survey on Frequent Itemset Mining

Abstract

Frequent itemset mining is the technique used mostly in field of data mining like finance, health care system. We are focusing on methodologies for extracting the useful knowledge from given data by using frequent itemset mining. Most important use of FIM is customer segmentation in marketing, shopping cart analyzes management relationship, web usage mining, and player tracking and so on. The time required for generating frequent itemsets plays an important role. Some algorithms like Apriori, Eclat, FP-Growth are designed, considering only the time factor. Our study includes depth analysis of algorithms and discusses some problems of generating frequent itemsets from the algorithm. We have explored the unifying feature among the internal working of various mining algorithms. The comparative study of algorithms includes aspects like different support values, size of transactions and different datasets.