Data mining represents the process of extracting interesting and previously unknown knowledge (patterns) from data. 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. An association rule expresses the dependence of a set of attribute-value pairs, called items, upon another set of items (item set).The association rule mining algorithms can be classified into two main groups: the level-wise algorithms and the tree-based algorithms. The level-wise algorithms scan the entire database multiple time but they have moderate memory requirement. The two phase algorithms scan the database only twice but they can have extremely large memory requirement. In this research, Performance study has been done which shows the advantages and disadvantage of algorithms used in association rules mining FP growth, Prepost+ and FIN. The main goal of this research is to explore the overview of the current research being carried out using the data mining techniques.