Object-Neighbourhood based Clustering Ensemble Method

Tahani Alqurashi, Wenjia Wang

Research output: Contribution to conferencePaper

Abstract

Clustering is an unsupervised learning and clustering results
are often inconsistent and unreliable when dierent clustering algorithms
are used. In this paper we have proposed a clustering ensemble frame-
work, named Object-Neighbourhood Clustering Ensemble (ONCE), to
improve the consistency, reliability and quality of the clustering result.
The core of the ONCE is a new consensus function that addresses the
uncertain agreements between members by taking the neighbourhood
relationship between object pairs into account in the similarity matrix.
The experiments are carried out on 11 benchmark datasets. The results
show that our ensemble method outperforms the co-association method,
when the Average linkage is used. Furthermore, the results show that
our ensemble method is more accurate than the baseline algorithm, and
this indicates that the clustering ensemble method is more consistent
and reliable than a single clustering algorithm.
Original languageEnglish
Publication statusPublished - 10 Sep 2014
EventThe 15th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2014) - Salamanca, Spain
Duration: 9 Sep 201412 Sep 2014

Conference

ConferenceThe 15th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2014)
CountrySpain
CitySalamanca
Period9/09/1412/09/14

Keywords

  • Machine Learning
  • Data mining
  • Clustering
  • Clsutering Ensemble

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