A unifying analysis for the supervised descriptive rule discovery via the weighted relative accuracy

摘要

Supervised descriptive rule discovery represents a set of data mining techniques whose objective is to describe data with respect to a property of interest. This concept encompasses different techniques such as subgroup discovery, emerging patterns and contrast sets. Supervised learning is used to obtain rules for descriptive purposes but with different quality measures. Although their origin is based on different data mining tasks, our hypothesis is about the existence of a compatibility between subgroup discovery, emerging patterns and contrast sets thanks to the common use of a weighted relative accuracy quality measure. A complete analysis shows this relationship between the different tasks. The analysis is supported by an empirical study with the most representative algorithms for each technique.