SciCSM

Abstract

Contrast set mining is a broadly applicable exploratory technique, which identifies interesting differences across contrast groups. The existing algorithms primarily target relational datasets with categorical attributes. There is clearly a need to apply this method to discover interesting patterns across scientific datasets, which feature arrays with numeric values. In this paper, we present a novel algorithm, SciCSM, for efficient contrast set mining over array-based datasets. We define how "interesting" contrast sets can be characterized for numeric and array data -- handling the fact that subsets can involve both value-based and/or dimension-based attributes. We extensively use bitmap indices to reduce computational complexity and enable processing of larger-scale data. We demonstrate both high efficiency and effectiveness of our algorithm by using multiple real-life datasets.