Data mining of association rules from items in transaction databases has been studied extensively in recent years. However these algorithms deal with only transactions with binary values whereas transactions with quantitative values are more commonly seen in real-world applications. As to fuzzy data mining, many approaches have also been proposed for mining fuzzy association rules. Most of the previous approaches, however, set a single minimum support threshold for all the items or itemsets and identify the relationships among transactions. In real applications, different items may have different criteria to judge their importance and quantitative data may exist. Thus the fuzzy data mining approaches are divided into two types, namely single-minimum-support fuzzy-mining (SSFM) and multiple-minimum-support fuzzy-mining (MSFM) problems. These algorithms integrates fuzzy set concepts and the apriori mining algorithm to find fuzzy association rules in given transaction data sets.

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K-means Clustering Data Mining Fuzzy Set Genetic Algorithm Fuzzy Association Rules Quantitative Transactions