Li, Rui, Perneczky, Robert ORCID: 0000-0003-1981-7435, Drzezga, Alexander and Kramer, Stefan (2015). Efficient redundancy reduced subgroup discovery via quadratic programming. J. Intell. Inf. Syst., 44 (2). S. 271 - 289. DORDRECHT: SPRINGER. ISSN 1573-7675

Full text not available from this repository.

Abstract

Subgroup discovery is a task at the intersection of predictive and descriptive induction, aiming at identifying subgroups that have the most unusual statistical (distributional) characteristics with respect to a property of interest. Although a great deal of work has been devoted to the topic, one remaining problem concerns the redundancy of subgroup descriptions, which often effectively convey very similar information. In this paper, we propose a quadratic programming based approach to reduce the amount of redundancy in the subgroup rules. Experimental results on 12 datasets show that the resulting subgroups are in fact less redundant compared to standard methods. In addition, our experiments show that the computational costs are significantly lower than the costs of other methods compared in the paper.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Li, RuiUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Perneczky, RobertUNSPECIFIEDorcid.org/0000-0003-1981-7435UNSPECIFIED
Drzezga, AlexanderUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Kramer, StefanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-409112
DOI: 10.1007/s10844-013-0284-1
Journal or Publication Title: J. Intell. Inf. Syst.
Volume: 44
Number: 2
Page Range: S. 271 - 289
Date: 2015
Publisher: SPRINGER
Place of Publication: DORDRECHT
ISSN: 1573-7675
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
Computer Science, Artificial Intelligence; Computer Science, Information SystemsMultiple languages
URI: http://kups.ub.uni-koeln.de/id/eprint/40911

Downloads

Downloads per month over past year

Altmetric

Export

Actions (login required)

View Item View Item