NDLI: Neural gas based cluster ensemble algorithm and its application to cancer data

Content Provider	IEEE Xplore Digital Library
Author	Zhiwen Yu You, J. Guihua Wen
Copyright Year	2011
Description	Author affiliation: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China (Zhiwen Yu; Guihua Wen) \|\| Department of Computing, Hong Kong Polytechnic University, Hong Kong (You, J.)
Abstract	The cluster ensemble approach is gaining more and more attention in recent years due to its useful applications in bioinformatics and pattern recognition. In this paper, we present a new cluster ensemble approach named as the neural gas based cluster ensemble algorithm (NGCEA) for class discovery from biological meaningful data, NGCEA first adopts the perturbed function to generate a set of new datasets. Then, it proposes to adopt the neural gas algorithm to obtain the clustering solutions from the perturbed datasets, In the following, NGCEA views the row of each clustering solution as the new features, and forms a new dataset. Finally, it adopts the neural gas algorithm as consensus function to perform clustering again on the new dataset and obtains the final result. The experiments in cancer datasets show that (i)NGCEA works well on most of cancer datasets (ii) NGCEA outperforms most of the state-of-the-art cluster ensemble algorithms when applied to gene expression data
Starting Page	15
Ending Page	20
File Size	503543
Page Count	6
File Format	PDF
ISBN	9781457703058
ISSN	21601348
e-ISBN	9781457703089
DOI	10.1109/ICMLC.2011.6016705
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Argon Biology Cancer data Cluster ensemble Class discovery
Content Type	Text
Resource Type	Article

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