Soft Computing

, Volume 22, Issue 18, pp 5935–5954 | Cite as

Aggregation of multi-objective fuzzy symmetry-based clustering techniques for improving gene and cancer classification

  • Sriparna SahaEmail author
  • Ranjita Das
  • Partha Pakray


The current work reports about the application of a cluster ensemble approach in combining results produced by some multiobjective-based clustering techniques. Firstly, some multiobjective-based fuzzy clustering techniques are developed using the search capabilities of differential evolution and particle swarm optimization. Both these clustering techniques utilize a recently developed point symmetry-based distance for allocation of points to different clusters. The appropriate partitioning from a data set is identified by optimizing simultaneously two cluster quality measures, namely Xie–Beni index and FSym-index. First objective function uses Euclidean distance as a similarity measure, and the second objective function uses point symmetry-based distance in its computation. A set of trade-off solutions are produced by each of these clustering techniques on the final Pareto optimal front. Finally, this set of solutions are combined using a link-based cluster ensemble technique. The effectiveness of ensemble techniques is illustrated on partitioning some real-life gene expression and cancer data sets where automatic identification of set of genes or set of cancer tissues is a pressing issue. The potency of the ensemble techniques applied on both the multi-objective DE- and PSO-based clustering approaches is shown in comparison with several state-of-the-art techniques.


Unsupervised classification Cluster ensemble Multi-objective particle swarm optimization Multi-objective differential evolution Symmetry Gene expression data Cancer data classification 



Authors would like to acknowledge the help from Indian Institute of Technology Patna and National Institute of Technology Mizoram to conduct this research.

Compliance with ethical standards

Conflict of interest

All the authors declare that they do not have any conflict of interest.

Human and animal rights

We have not performed any experiments which involve animals or humans.


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology PatnaPatnaIndia
  2. 2.Department of Computer Science and EngineeringNational Institute of Technology MizoramAizawlIndia

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