An ensemble of locally reliable cluster solutions

Huan Niu, Nasim Khozouie, Hamid Parvin*, Hamid Alinejad-Rokny, Amin Beheshti, Mohammad Reza Mahmoudi

*Corresponding author for this work

Research output: Contribution to journalArticle

8 Citations (Scopus)
4 Downloads (Pure)


Clustering ensemble indicates to an approach in which a number of (usually weak) base clusterings are performed and their consensus clustering is used as the final clustering. Knowing democratic decisions are better than dictatorial decisions, it seems clear and simple that ensemble (here, clustering ensemble) decisions are better than simple model (here, clustering) decisions. But it is not guaranteed that every ensemble is better than a simple model. An ensemble is considered to be a better ensemble if their members are valid or high-quality and if they participate according to their qualities in constructing consensus clustering. In this paper, we propose a clustering ensemble framework that uses a simple clustering algorithm based on kmedoids clustering algorithm. Our simple clustering algorithm guarantees that the discovered clusters are valid. From another point, it is also guaranteed that our clustering ensemble framework uses a mechanism to make use of each discovered cluster according to its quality. To do this mechanism an auxiliary ensemble named reference set is created by running several kmeans clustering algorithms.

Original languageEnglish
Article number1891
Pages (from-to)1-20
Number of pages20
JournalApplied Sciences (Switzerland)
Issue number5
Publication statusPublished - 1 Mar 2020

Bibliographical note

Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • Ensemble clustering
  • Ensemble learning
  • Kmedoids clustering
  • Local hypothesis

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