Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23679
Title: Hybrid clustering solution selection strategy
Authors: Yu, Z
Li, L
Gao, Y
You, J 
Liu, J
Wong, HS
Han, G
Keywords: Cluster ensemble
Clustering solution selection
Feature selection
Hybrid strategy
Issue Date: 2014
Publisher: Elsevier
Source: Pattern recognition, 2014, v. 47, no. 10, p. 3362-3375 How to cite?
Journal: Pattern recognition 
Abstract: Cluster ensemble approaches make use of a set of clustering solutions which are derived from different data sources to gain a more comprehensive and significant clustering result over conventional single clustering approaches. Unfortunately, not all the clustering solutions in the ensemble contribute to the final result. In this paper, we focus on the clustering solution selection strategy in the cluster ensemble, and propose to view clustering solutions as features such that suitable feature selection techniques can be used to perform clustering solution selection. Furthermore, a hybrid clustering solution selection strategy (HCSS) is designed based on a proposed weighting function, which combines several feature selection techniques for the refinement of clustering solutions in the ensemble. Finally, a new measure is designed to evaluate the effectiveness of clustering solution selection strategies. The experimental results on both UCI machine learning datasets and cancer gene expression profiles demonstrate that HCSS works well on most of the datasets, obtains more desirable final results, and outperforms most of the state-of-the-art clustering solution selection strategies.
URI: http://hdl.handle.net/10397/23679
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2014.04.005
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

6
Last Week
0
Last month
0
Citations as of Aug 15, 2017

WEB OF SCIENCETM
Citations

5
Last Week
0
Last month
0
Citations as of Aug 14, 2017

Page view(s)

49
Last Week
1
Last month
Checked on Aug 13, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.