Efficient mining of association rules based on clustering from distributed data

Abstract

Data analysis techniques need to be improved to allow the processing of data. One of the most commonly used techniques is the Association Rule Mining. These rules are used to detect facts that often occur together within a dataset. Unfortunately, existing methods generate a large number of association rules, without accentuation on the relevance and utility of these rules, and hence, complicating the results interpretation task. In this paper, we propose a new approach for mining association rules with an emphasis on easiness of assimilation and exploitation of the carried knowledge. Our approach addresses these shortcomings, while efficiently and intelligently minimizing the rules size. In fact, we propose to optimize the size of the extraction contexts taking advantages of the Clustering techniques. We then extract frequent itemsets and rules in the form of Meta-itemsets and Meta-rules, respectively. Experiments on benchmarking datasets show that our approach leads to a significant reduction of the number of generated rules thereby speeding up the execution time.