Subgroup discovery with CN2-SD

N Lavrač, B Kavšek, PA Flach, L Todorovski

Research output: Contribution to journalArticle (Academic Journal)peer-review

298 Citations (Scopus)


This paper investigates how to adapt standard classification rule learning approaches to subgroup discovery. The goal of subgroup discovery is to find rules describing subsets of the population that are sufficiently large and statistically unusual. The paper presents a subgroup discovery algorithm, CN2-SD, developed by modifying parts of the CN2 classification rule learner: its covering algorithm, search heuristic, probabilistic classification of instances, and evaluation measures. Experimental evaluation of CN2-SD on 23 UCI data sets shows substantial reduction of the number of induced rules, increased rule coverage and rule significance, as well as slight improvements in terms of the area under ROC curve, when compared with the CN2 algorithm. Application of CN2-SD to a large traffic accident data set confirms these findings.
Translated title of the contributionSubgroup discovery with CN2-SD
Original languageEnglish
Pages (from-to)153 - 188
Number of pages36
JournalJournal of Machine Learning Research
Publication statusPublished - Feb 2004

Bibliographical note

Publisher: Microtome Publishing


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