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International Conference on Mining Intelligence and Knowledge Exploration

MIKE 2017: Mining Intelligence and Knowledge Exploration pp 35–42Cite as

A Betweenness Centrality Guided Clustering Algorithm and Its Applications to Cancer Diagnosis

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10682))

Abstract

Clustering has become one of the important data analysis techniques for the discovery of cancer disease. Numerous clustering approaches have been proposed in the recent years. However, handling of high-dimensional cancer gene expression datasets remains an open challenge for clustering algorithms. In this paper, we present an improved graph based clustering algorithm by applying edge betweenness criterion on spanning subgraph. We carry out empirical analysis on artificial datasets and five cancer gene expression datasets. Results of the study show that the proposed algorithm can effectively discover the cancerous tissues and it performs better than two recent graph based clustering algorithms in terms of cluster quality as well as modularity index.

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Authors and Affiliations

  1. Department of Computer Engineering, School of Technology, Pandit Deendayal Petroleum University, Gandhinagar, India

    R. Jothi

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  1. R. Jothi
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Correspondence to R. Jothi .

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Editors and Affiliations

  1. Indian Statistical Institute, Kolkata, India

    Ashish Ghosh

  2. Institute for Development and Research in Banking Technology, Hyderabad, India

    Rajarshi Pal

  3. Indian Institute of Information Technology, Sri City, India

    Rajendra Prasath

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Jothi, R. (2017). A Betweenness Centrality Guided Clustering Algorithm and Its Applications to Cancer Diagnosis. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-71928-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71927-6

  • Online ISBN: 978-3-319-71928-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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