A weight-incorporated similarity-based clustering ensemble method based on swarm intelligence

Qi Kang, Shiyao Liu, Mengchu Zhou, Sisi Li

Research output: Contribution to journalArticlepeer-review

57 Scopus citations


Clustering methods play an important role in data mining and various other applications. This work investigates them based on swarm intelligence. It proposes a new clustering method by combining K-means clustering method and mussels wandering optimization algorithm. A single cluster method is well recognized to achieve limited performance when it is compared with a clustering ensemble (CE) that integrates several single ones. Hence, this work introduces a new CE method called weight-incorporated similarity- based CE. The commonly-used datasets with varying size are used to test the performance of the proposed methods. The simulation results illustrate the validity and performance advantages of the proposed ones over some of their peers.

Original languageEnglish (US)
Pages (from-to)156-164
Number of pages9
JournalKnowledge-Based Systems
StatePublished - Jul 15 2016

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems and Management
  • Artificial Intelligence
  • Management Information Systems


  • Clustering ensemble
  • Data clustering
  • Optimization
  • Swarm intelligence


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