Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
Association rules that express relations between itemsets is effective in the field of data mining such as basket analysis. However, the number of rules tends to be huge for actual dataset, which makes it difficult for an analyst to understand the mining result. To solve this problem, the information visualization is expected to be useful for the analysis. This paper proposes visualizing method that can be applied to large dataset. The proposed method focuses on analyzing the combination of item categories in extracted itemset, and visualizes frequent closet itemsets that are extracted in the previous step of extracting association rules. A prototype system based on the proposed method is implemented, which shows that insights into the combinations of item categories can be obtained. finding concerning the combination of the item categories.
