Decision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned

N Lavrac, B Cestnik, D Gamberger, PA Flach

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

66 Citations (Scopus)

Abstract

This paper presents ways to use subgroup discovery to generate actionable knowledge for decision support. Actionable knowledge is explicit symbolic knowledge, typically presented in the form of rules, that allows the decision maker to recognize some important relations and to perform an appropriate action, such as targeting a direct marketing campaign, or planning a population screening campaign aimed at detecting individuals with high disease risk. Different subgroup discovery approaches are outlined, and their advantages over using standard classification rule learning are discussed. Three case studies, a medical and two marketing ones, are used to present the lessons learned in solving problems requiring actionable knowledge generation for decision support.
Translated title of the contributionDecision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned
Original languageEnglish
Pages (from-to)115 - 143
Number of pages28
JournalMachine Learning
Volume57(1-2)
Publication statusPublished - Oct 2004

Fingerprint Dive into the research topics of 'Decision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned'. Together they form a unique fingerprint.

Cite this