Nugget Browser: Visual Subgroup Mining and Statistical Significance Discovery in Multivariate Datasets

Discovering interesting patterns in datasets is a very important data mining task. Subgroup patterns are local findings identifying the subgroups of a population with some unusual, unexpected, or deviating distribution of a target attribute. However, this pattern discovery task poses several compelling challenges. First, computational data mining techniques can generally only discover and extract pre-defined patterns. Second, since the extracted patterns are typically multi-dimensional arbitrary-shaped regions, it is very difficult to convey in an easily interpretable manner. Finally, in order to assist analysts in exploring their discoveries and understanding the relationships among patterns, as well as connections between patterns and the underlying data instances, an integrated visualization system is greatly needed. In this paper, we present a novel subgroup pattern extraction and visualization system, called the Nugget Browser, that takes advantage of both data mining methods and interactive visual exploration. The system accepts analysts´ mining queries interactively, converts the query results into an understandable form, builds visual representations, and supports navigation and exploration for further analyses.