A Quantitative Association Rule Mining Algorithm Based on Clustering Algorithm

In order to develop a data mining system for huge database mainly composed of numerical attributes, there exists necessary process to decide valid quantization of the numerical attributes. Though the clustering algorithm can provide useful information for the quantization problem, it is difficult to formulate appropriate clusters for rule extraction in terms of cluster size and shape. In this paper, we propose a new method of quantitative association rule extraction that can quantize the attribute by applying clustering algorithm and extract rules simultaneously. From the results of numerical experiments using benchmark data, the method is found to be promised for actual applications.