NG2CE: Double neural gas based cluster ensemble framework

Hantao Chen, Zhiwen Yu, Guoqiang Han, Jia You, Le Li

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

6 Citations (Scopus)

Abstract

Though there exist different kinds of cluster ensemble methods, few of them consider how to process the dataset with noisy attributes. In this paper, we design a double neural gas based cluster ensemble framework, named as NG2CE, which is a new cluster ensemble framework to process the dataset with noisy attributes. Compared with traditional cluster ensemble methods, the cluster framework NG2CE not only adopts the neural gas algorithm to perform clustering on the data dimension, but also applies the neural gas algorithm to the attribute dimension, which will increase the diversity of the ensemble and improve the accuracy of the final result. NG2CE also adopts the normalized cut algorithm to partition the consensus matrix and obtains the final result. The results of the experiments on both synthetic datasets and real datasets show that (i) NG2CE works well on both synthetic datasets and real datasets. (ii) NG2CE is able to improve the accuracy and the stableness of the final result.
Original languageEnglish
Title of host publicationICCSE 2012 - Proceedings of 2012 7th International Conference on Computer Science and Education
Pages26-31
Number of pages6
DOIs
Publication statusPublished - 5 Nov 2012
Event2012 7th International Conference on Computer Science and Education, ICCSE 2012 - Melbourne, VIC, Australia
Duration: 14 Jul 201217 Jul 2012

Conference

Conference2012 7th International Conference on Computer Science and Education, ICCSE 2012
CountryAustralia
CityMelbourne, VIC
Period14/07/1217/07/12

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Theoretical Computer Science

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