Association Reducts: Complexity and Heuristics

descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Conference object , Other literature type 01 Jan 2006Publisher:Springer Berlin Heidelberg

Authors: Dominik Slezak;

doi: 10.1007/11908029_18

Association Reducts: Complexity and Heuristics

- Summary
- Metrics

Abstract

We investigate association reducts, which extend previously studied information and decision reducts in capability of expressing dependencies between groups of attributes in data. We formulate optimization problems related to the most informative associations between groups of attributes. We provide heuristic mechanisms for addressing those problems. We also discuss at more general level how to express approximate dependencies between groups of attributes.

Related Organizations

Polish-Japanese Academy of Information Technology
Poland
University of Regina
Canada

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	7
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%