1. Introduction
- Motivation
- understanding differences between groups
- Task
- provide an efficient algorithm for mining contrast contrast sets and pruning rules to reduce complexity
- provide post processing techniques to present subsets that are surprising
- control the false positives
- be statistically sound
- Goal
- To find contrast-sets whose support differs meaningfully (statistically) across groups
, 
2. Naive Approach
- Add an attribute to the set (group type) and use Association Rule Mining to find the differences
- Problems
- this will not return group differences
- the results will be difficult to interpret