Frequent pattern using Multiple Attribute Value for itemset generation

Data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. While Association Rules Mining (ARM) algorithm especially the Apriori algorithm has been an active research work in recent years. Diverse improvement varies in term of producing more frequent items and also generating further k-length. The idea is to produce better pattern and more interesting rules. In this paper, we propose new approach for ARM based on Multiple Attribute Value within the non-binary search spaces. The proposed algorithm improves the existing frequent pattern mining by generating the most frequent values (item) within the attribute and generate candidate based on the frequent attribute value. The main idea of our work is to discover more meaningful frequent items and maximum k-length items. The experimental results show that our proposed MAV frequent pattern mining enhance the impact in generating more frequents items and maximum length.