Evaluation of Fitness Functions for Swarm Clustering Applied to Gene Expression Data

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)

Abstract

Clustering problem is being studied by many of the researchers using swarm intelligence. However, the search space is not carried out entirely randomly; a proper fitness function is required to determine the next step in the search space. This paper studies Particle Swarm Optimization (PSO) based clustering with two different fitness functions namely Xie-Beni and Davies-Bouldin indices for brain tumor gene expression dataset. Clustering results are validated using Mean Absolute Error (MAE) and Dunn Index (DI). To analyze function of genes, genes that have similar expression patterns should be grouped and the datasets should be presented to the physicians in a meaningful way. High usability of algorithm and the encouraging results suggests that swarm clustering (PSO based clustering) with Davies-Bouldin index as fitness functions with respect to Dunn index can be a practical tool for analyzing gene expression patterns.

Keywords

Swarm clustering PSO Xie-Beni Davies-Bouldin Gene clustering 

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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.B. S. Abdur Rahman UniversityChennaiIndia
  2. 2.Mahendra Engineering CollegeNamakkalIndia

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