A Survey on Fuzzy Association Rule Mining

Abstract

Association rule mining is one of the fundamental tasks of data mining. The conventional association rule mining algorithms, using crisp set, are meant for handling Boolean data. However, in real life quantitative data are voluminous and need careful attention for discovering knowledge. Therefore, to extract association rules from quantitative data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type. In the sequel, it may suffer with the problem of sharp boundary. Hence, fuzzy association rules are developed as a sharp knife to solve the aforesaid problem by handling quantitative data using fuzzy set. In this paper, the authors present an updated survey of fuzzy association rule mining procedures along with a discussion and relevant pointers for further research.