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A Clustering Ensemble Based Unsupervised Feature Selection Approach(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2007年03期
Page:
60-63
Research Field:
Publishing date:

Info

Title:
A Clustering Ensemble Based Unsupervised Feature Selection Approach
Author(s):
Ling Xiaohan; Ji Genlin
School of Mathematics and Computer Science,Nanjing Normal University,Nanjing 210097,China
Keywords:
feature se lection;  unsuperv ised learn ing;  ensem ble lea rning
PACS:
TP311.13
DOI:
-
Abstract:
An unsuperv ised fea ture se lection approach is proposed, wh ich utilizes c luste ring to obta in the c lass labe l o f data ob ject and uses ensemb le techn ique to reso lve the instab ility o f cluster ing. As c lustering resu lts generated by som e a lgo rithm s are usually different from each other, feature se lection perfo rm sm ultip ly and all results are com b ined to produce fina l se lected fea tures. In addition, Relie fF is ame liorated, w hich is a superv ised fea ture selection a lgorithm and is em ployed as an essentia l part in the approach. Exper imenta l resu lts show that the approach can rem ove redundan t features and improve the quality of c lustering.

References:

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Last Update: 2013-06-04