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Impact of Partition Based Clustering Algorithms to Cluster Samples in Microarray Gene Expression Data

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Intelligent Techniques and Applications in Science and Technology (ICIMSAT 2019)

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 12))

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Abstract

The huge amount of gene expression profile data produced from DNA Microarray Technology has forced the analysis procedure to be applied in multiple biomedical fields. Analysis of cancer data for proper diagnosis of cancer is an important field where early detection of cancer or different levels of cancer helps in early recovery of cancer diseases. So, sample classification has become an evident task for this analysis. Clustering is an important process that can be applied in identification of new subtypes of cancer. Partition based clustering algorithms are popular due to their simplicity and ability to provide moderate results in most of the cases. In this regard here a comparative performance analysis has been performed to show the impact of partition based clustering algorithms to cluster samples in microarray data. In this paper, five classical and popular partition based algorithms are applied on eight gene expression datasets to illustrate the comparative performances. The results show the usefulness of the selected partition based algorithms clearly.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, 700152, West Bengal, India

    Chandra Das, Shilpi Bose, Debanjana Karmakar, Agniswar Roy & Abhik Banerjee

  2. Department of Information Technology, Netaji Subhash Engineering College, Kolkata, 700152, West Bengal, India

    Natasha Ghosh

  3. School of Education Technology, Jadavpur University, Kolkata, West Bengal, India

    Matangini Chattopadhyay

Authors
  1. Chandra Das
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  2. Shilpi Bose
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  3. Debanjana Karmakar
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  4. Agniswar Roy
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  5. Natasha Ghosh
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  6. Abhik Banerjee
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  7. Matangini Chattopadhyay
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Corresponding author

Correspondence to Chandra Das .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Siliguri Institute of Technology, Sukna, West Bengal, India

    Subhojit Dawn

  2. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Arad, Romania

    Valentina Emilia Balas

  3. Department of Psychology and IIASS, Università della Campania “Luigi Vanvitelli”, Caserta, Caserta, Italy

    Anna Esposito

  4. Mizoram University, Aizawl, Mizoram, India

    Sadhan Gope

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Das, C. et al. (2020). Impact of Partition Based Clustering Algorithms to Cluster Samples in Microarray Gene Expression Data. In: Dawn, S., Balas, V., Esposito, A., Gope, S. (eds) Intelligent Techniques and Applications in Science and Technology. ICIMSAT 2019. Learning and Analytics in Intelligent Systems, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-42363-6_77

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