FCM for Gene Expression Bioinformatics Data

  • Kumar Dhiraj
  • Santanu Kumar Rath
  • Korra Sathya Babu
Part of the Communications in Computer and Information Science book series (CCIS, volume 40)


Clustering analysis of data from DNA microarray hybridization studies is essential for evaluating and identifying biologically significant co-expressed genes. The K-means algorithm is one of the most widely used clustering technique. It attempts to solve the clustering problem by assigning each gene to a single cluster. However, in practice especially in case of Bioinformatics data, one gene can be found in many clusters simultaneously. To sort out this problem, Fuzzy C-means (FCM) clustering algorithm is applied to microarray data. Two pattern recognition data (IRIS and WBCD data) and thirteen microarray data is used to evaluate performance of K-means and Fuzzy C-means. Improvement of approx. 30 percent clustering accuracy is achieved in case of FCM compared to K-means algorithm. Extensive simulation results shows that the FCM clustering algorithm was able to provide the highest accuracy and generalization results compared to K-means clustering algorithm.


K-means clustering Fuzzy c-means Microarray Cancer data Gene Expression Analysis Bioinformatics 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kumar Dhiraj
    • 1
  • Santanu Kumar Rath
    • 1
  • Korra Sathya Babu
    • 1
  1. 1.Dept of Computer science and EngineeringNational Institute of Technology RourkelaIndia

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