A Survey on Different Association Rule Mining Algorithms in Data Mining

Abstract Recently, the importance of database mining is growing at an extremely fast pace due to the increasing use of computing for various applications. One of the most important data mining problems is mining association rules. Association rule mining is a procedure that is meant to find frequent patterns, correlations, associations or causal structures from data sets found in various kinds of databases such as relational databases, transactional databases and other forms of data repositories. An association rule has two parts, an antecedent and a consequent. An antecedent is an item found in the data. A consequent is an item that is found in combination with the antecedent. In data mining, association rules are useful for analyzing and predicting customer behavior. Hence, this paper, presents the study of various association rule mining and then discuss about the previous researches which are associated with the association rule mining. Moreover, this paper gives the overview of the various association rule mining algorithms, including Apriori, Eclat, FP-growth algorithms and its comparisons. The main objective of this research is to study about different association rule mining algorithms. Finally, comparisons are made in association rule mining in terms of merits, demerits, outcomes and data sets. Keywords: Data mining, Association rules and Databases.