FARM: a data mining system for discovering fuzzy association rules

In this paper, we introduce a novel technique, called FARM, for mining fuzzy association rules. FARM employs linguistic terms to represent the revealed regularities and exceptions. The linguistic representation is especially useful when those rules discovered are presented to human experts for examination because of the affinity with the human knowledge representations. The definition of linguistic terms is based on fuzzy set theory and hence we call the rules having these terms fuzzy association rules. The use of fuzzy technique makes FARM resilient to noises such as inaccuracies in physical measurements of real-life entities and missing values in the databases. Furthermore, FARM utilizes adjusted difference analysis which has the advantage that it does not require any user-supplied thresholds which are often hard to determine. In addition to this interestingness measure, FARM has another unique feature that the conclusions of a fuzzy association rule can contain linguistic terms. Our technique also provides a mechanism to allow quantitative values be inferred from fuzzy association rules. Unlike other data mining techniques that can only discover association rules between different discretized values, FARM is able to reveal interesting relationships between different quantitative values. Our experimental results showed that FARM is capable of discovering meaningful and useful fuzzy association rules in an effective manner from a real-life database.