Mining fuzzy similar association rules from quantitative data

Data mining of association rules from items in transaction databases has been studied extensively in recent years. In order to discover more practical rules, domain knowledge such as taxonomies of items [9] and similarity among items [11] have been considered to produce generalized association rules and similar association rules respectively. However, these algorithms deal with only transactions with binary values whereas transactions with quantitative values are more commonly seen in real-world applications. This paper thus proposes a new data-mining algorithm for extracting fuzzy knowledge from transactions stored as quantitative values. The proposed algorithm integrates fuzzy set concepts and the a priori mining algorithm to find fuzzy similar association rules in given transaction data sets where similarity relations are assumed among database items. The rules discovered here thus promote coarser granularity of association rules and exhibit quantitative regularity under similarity relations.