Improving Contrast Set Mining

A fundamental task in exploratory data analysis is discerning the differences between contrasting groups. Contrast set mining has been developed as a data mining task, which aims to identify the differences between these groups. This paper examines the algorithms, heuristics, and open issues of contrast set mining, and seeks to improve contrast set mining by addressing several of the open issues. It proposes four interestingness measures for ranking contrast sets: coverage, overall support, growth rate, and unusualness. It introduces a new method to discretize quantitative attributes. A new type of contrast set, called the jumping contrast set, is defined, and the contrast set mining process is modified, to include mining both types of contrast sets, on datasets containing both quantitative and categorical attributes. Finally, a simple visualization method is introduced, to describe contrast sets to the end-user.