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Title: Neural gas based cluster ensemble algorithm and its application to cancer data
Authors: Yu, Z
You, J 
Wen, G
Keywords: Cancer data
Class discovery
Cluster ensemble
Issue Date: 2011
Publisher: IEEE
Source: 2011 International Conference on Machine Learning and Cybernetics (ICMLC), 10-13 July 2011, Guilin, p. 15-20 How to cite?
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.
ISBN: 978-1-4577-0305-8
ISSN: 2160-133X
DOI: 10.1109/ICMLC.2011.6016705
Appears in Collections:Conference Paper

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