Frequent itemset mining algorithms: A literature survey

Data mining is used for mining useful data from huge datasets and finding out meaningful patterns from the data. Many organizations are now using data mining techniques. Frequent pattern mining has become an important data mining technique and has been a focused area in research field. Frequent patterns are patterns that appear in a data set most frequently. Various methods have been proposed to improve the performance of frequent pattern mining algorithms. In this paper, we provide the preliminaries of basic concepts about frequent pattern tree(fp-tree) and present a survey of the recent developments in this area that is receiving increasing attention from the Data Mining community. Experimental results show that fp- Tree based approach achieves better performance than Apriori. So here we concentrate on recent fp-tree modifications and some other new techniques other than Apriori. A single paper cannot be a complete review of all the algorithms, here we have included only four relevant papers which are recent and directly using the basic concept of fp-tree. A brief description of each technique has been provided. This detailed literature survey is a preliminary to the proposed research which is to be further carried on.