An algorithm for mining fuzzy association rules

Fuzzy association rules described by the natural language are well suited for the thinking of human subject and will help to increase the flexibility for supporting user in making decisions or designing the fuzzy systems. However, the efficiency of algorithms needs to be improved to handle huge datasets in real word. in this paper, we present an efficient algorithm named Fuzzy Cluster-Based Association Rules(FCBAR). The FCBAR method is to create cluster tables by scanning the database once, and then clustering the transaction records to the k_th cluster table, where the length of a record is k. Moreover, the fuzzy large itemsets are generated by contrasts with the partial cluster tables. This prunes considerable amount of data, reduces the time needed to perform data scans and requires less contrast. Experiments with the real-life database show that FCBAR outperforms fuzzy Apriori_like algorithm, a well-known and widely used association rules algorithm.