An Efficient Artificial Bee Colony and Fuzzy C Means Based Co-regulated Biclustering from Gene Expression Data

  • K. Sathishkumar
  • E. Balamurugan
  • P. Narendran
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)

Abstract

The gene microarray data are arranged based on the pattern of gene expression using various clustering algorithms and the dynamic natures of biological processes are generally unnoticed by the traditional clustering algorithms. To overcome the problems in gene expression analysis, novel algorithms for finding the coregulated clusters, dimensionality reduction and clustering have been proposed. The coregulated clusters are determined using biclustering algorithm, so it is called as coregulated biclusters. The coregulated biclusters are two or more genes which contain similarity features. The dimensionality reduction of microarray gene expression data is carried out using Locality Sensitive Discriminant Analysis (LSDA). To maintain bond between the neighborhoods in locality, LSDA is used and an efficient meta heuristic optimization algorithm called Artificial Bee Colony (ABC) using Fuzzy C Means clustering is used for clustering the gene expression based on the pattern. The experimental results shows that proposed algorithm achieve a higher clustering accuracy and takes lesser less clustering time when compared with existing algorithms.

Keywords

Gene expression data Bimax Algorithm Co-regulated Biclusters Locality Sensitive Discriminant Analysis Artificial Bee Colony Fuzzy C Means 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • K. Sathishkumar
    • 1
  • E. Balamurugan
    • 2
  • P. Narendran
    • 1
  1. 1.Gobi Arts & Science CollegeGobichettipalayamIndia
  2. 2.Bannari Amman institute of TechnologySathyamangalamIndia

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