Visualization of Data Mining Rules using OLAP

Data Mining is an emerging knowledge discovery process of extracting pre viously unknown, actionable information from very large scientific and commercial databases. Usually, a data mining process extracts rules by processing high dimensional categorical and/or numerical data. However, in the data mining context the user often has to analyze hundreds of extracted rules in order to grasp valuable knowledge. Thus, the analysis of such rules by means of visual tools has evolved rapidly in recent years. Visual data mining attempts to take advantage of humans' ability to perceive pattern and structure in visual form. Researchers have developed many tools to visualize data mining rules. However, few of these tools can handle effectively more than a few dozens of data mining rules. In this paper, we propose a new visualization technique of data mining rules based on OLAP. More specifically, the proposed technique utilizes the standard two-dimensional cross-tabulation table of most OLAP models in order to visualize even a great number of data mining rules. The advantage of the proposed technique is that the user can choose to "drill down" on specific subsets of such rules. We also present experimental results that demonstrate how the proposed technique is useful and helpful for analyzing and understanding extracted data mining rules.

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