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

Zhiwen Yu, Jia You, Guihua Wen

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

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
Original languageEnglish
Title of host publicationProceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Pages15-20
Number of pages6
Volume1
DOIs
Publication statusPublished - 7 Nov 2011
Event2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China
Duration: 10 Jul 201113 Jul 2011

Conference

Conference2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Country/TerritoryChina
CityGuilin, Guangxi
Period10/07/1113/07/11

Keywords

  • Cancer data
  • Class discovery
  • Cluster ensemble

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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