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Comparative Survey on Association Rule Mining Algorithms

Association rule mining has become particularly popular among marketers. In fact, an example of association rule mining is known as market basket analysis. The task is to find which items are frequently purchased together. This knowledge can be used by professionals to plan layouts and to place items that are frequently bought together in close proximity to each other, thus helping to improve the sales. Association rule mining involves the relationships between items in a data set. Association rule mining classifies a given transaction as a subset of the set of all possible items. Association rule mining finds out item sets which have minimum support and are represented in a relatively high number of transactions. These transactions are simply known as frequent item sets. The algorithms that use association rules are divided into two stages, the first is to find the frequent sets and the second is to use these frequent sets to generate the association rules. In this paper the applications, merits and demerits of these algorithms have been studied. This paper discusses the respective characteristics and the shortcomings of the algorithms for mining association rules. It also provides a comparative study of different association rule mining techniques stating which algorithm is best suitable in which case.