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.

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The discovery of association rule acquire an imperative role in data mining since its inception, which tries to find correlation among the attributes in a database. Classical algorithms/procedures meant for Boolean data and they suffer from sharp boundary problem in handling quantitative data. Thereby fuzzy association rule (i.e., association rule based on fuzzy sets) with fuzzy minimum support and confidence is introduced as an alternative tool. Besides, rule length, comprehensibility, and interestingness are also potentially used as quality metrics. Additionally, in fuzzy association rule mining, determining number fuzzy sets, tuning membership functions and automatic design of fuzzy sets are prominent objectives. Hence fuzzy association rule mining problem can be viewed as a multi-objective optimisation problem. On the other side, multi-objective genetic algorithms are established and efficient techniques to uncover Pareto front. Therefore, to bridge these two fields of research many

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