Parallel reduction based on condition attributes

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 01 Nov 2011Publisher:IEEEJournal:2011 IEEE International Conference on Granular Computing

Authors: Dayong Deng; Dianxun Yan; Lin Chen;

doi: 10.1109/grc.2011.6122586

Parallel reduction based on condition attributes

- Summary
- Metrics

Abstract

In this paper, we propose a parallel algorithm to obtain reducts. The algorithm firstly divides the decision system into a number of subsystems, then reduce conditional attributes in each subsystem, and merge some subsystems together. This process repeats until all subsystems are merged into one. In this way, conditional attributes are reduced efficiently. The effectiveness of our method is analyzed and validated through experiments on seven datasets.

Related Organizations

Zhejiang Normal University
China (People's Republic of)

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).	1
	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).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average