Universität zu Köln

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:	Creators Email ORCID ORCID Put Code Li, Rui UNSPECIFIED UNSPECIFIED UNSPECIFIED Perneczky, Robert UNSPECIFIED orcid.org/0000-0003-1981-7435 UNSPECIFIED Drzezga, Alexander UNSPECIFIED UNSPECIFIED UNSPECIFIED Kramer, Stefan UNSPECIFIED UNSPECIFIED UNSPECIFIED
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:	Keywords Language Computer Science, Artificial Intelligence; Computer Science, Information Systems Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/40911