Visualization of similarities and dissimilarities in rules using multidimensional scaling

One of the most important problems with rule induction methods is that it is very difficult for domain experts to check millions of rules generated from large datasets. The discovery from these rules requires deep interpretation from domain knowledge. Although several solutions have been proposed in the studies on data mining and knowledge discovery, these studies are not focused on similarities between rules obtained. When one rule r(1) has reasonable features and the other rule r(2) with high similarity to r(1) includes unexpected factors, the relations between these rules will become a trigger to the discovery of knowledge. In this paper, we propose a visualization approach to show the similar and dissimilar relations between rules based on multidimensional scaling, which assign a two-dimensional cartesian coordinate to each data point from the information about similiaries between this data and others data. We evaluated this method on two medical data sets, whose experimental results show that knowledge useful for domain experts could be found.