A Cluster-Based Divide-and-Conquer Genetic-Fuzzy Mining Approach for Items with Multiple Minimum Supports

In this paper, an enhanced efficient approach for speeding up the evolution process for finding minimum supports, membership functions and fuzzy association rules is proposed by utilizing clustering techniques. All the chromosomes use the requirement satisfaction derived only from the representative chromosomes in the clusters and from their own suitability of membership functions to calculate the fitness values. The evaluation cost can thus be greatly reduced due to the cluster-based time-saving process. The final best minimum supports and membership functions in all the populations are then gathered together for mining fuzzy association rules. Experimental results also show the efficiency of the proposed approach.