A Tree-Based Framework for Difference Summarization
Ruoming Jin, Yuri J. Breitbart, Rong Li · 2009
Understanding the differences between two datasets is a fundamental data mining question and is also ubiquitously important across many real world scientific applications. In this paper, we propose a tree-based framework to provide a parsimonious explanation of the difference between two distributions based on rigorous two-sample statistical test. We develop two efficient approaches. The first one is a dynamic programming approach that finds a minimal number of data subsets that describe the difference between two data sets. The second one is a greedy approach that approximates the dynamic programming approach. We employ the well-known Friedman's MST (minimal spanning tree) statistics for two-sample statistical tests in our summarization tree construction, and develop novel techniques to speedup its computational procedure. We performed a detailed experimental evaluation on both real and synthetic datasets and demonstrated the effectiveness of our tree-summarization approach.