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FCM for Gene Expression Bioinformatics Data

  • Conference paper
Book cover Contemporary Computing (IC3 2009)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 40))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Dept of Computer science and Engineering, National Institute of Technology Rourkela, India, 769008

    Kumar Dhiraj, Santanu Kumar Rath & Korra Sathya Babu

Authors
  1. Kumar Dhiraj
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  2. Santanu Kumar Rath
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  3. Korra Sathya Babu
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Editor information

Editors and Affiliations

  1. Dept. of Computer Sciences, University of Florida, Gainesville, FL, 32611, USA

    Sanjay Ranka

  2. Laurence H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, USA

    Srinivas Aluru

  3. Grid Computing and Distributed Systems Laboratory and, NICTA Victoria Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Australia

    Rajkumar Buyya

  4. Department of Computer Science, National Tsing Hua University, Taiwan

    Yeh-Ching Chung

  5. Computer Science, College of Engineering and Science, Louisiana Tech University, Ruston, LA, 71272, USA

    Sumeet Dua  & Vir V. Phoha  & 

  6. Department of Computer Sciences, Purdue University, W. Lafayette, IN, 47907, USA

    Ananth Grama

  7. Arizona State University, Tempe, AZ, 85281, USA

    Sandeep K. S. Gupta

  8. Computer Science and Engineering Department, Indian Institute of Technology Kharagpur, 721 302, WB, India

    Rajeev Kumar

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© 2009 Springer-Verlag Berlin Heidelberg

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Dhiraj, K., Rath, S.K., Babu, K.S. (2009). FCM for Gene Expression Bioinformatics Data. In: Ranka, S., et al. Contemporary Computing. IC3 2009. Communications in Computer and Information Science, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03547-0_50

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  • DOI: https://doi.org/10.1007/978-3-642-03547-0_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03546-3

  • Online ISBN: 978-3-642-03547-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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