Association Rule Clustering Based on Taxonomy Information

Association rule mining often produces a large number of rules. To facilitate exploratory analysis, structuring of rules is needed. A useful method for structuring rules is clustering. All of the existing methods for clustering rules suffer from the costly scan of the original dataset for determining the distances between rules. Moreover, the result of these methods is a fixed number of clusters that makes exploratory analysis difficult. A new method is proposed to overcome these problems. Taxonomy information is used to measure the distances between rules and the expensive scan of the original dataset is avoided. A Clustering algorithm, OPTICS, is applied to generate the clustering structure suitable for exploratory analysis. Finally, an experiment is conducted on a real-life dataset and the experimental result is presented via a visualization tool, which shows that the method is practical and effective.