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DK-means: a deterministic K-means clustering algorithm for gene expression analysis

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Abstract

Clustering has been widely applied in interpreting the underlying patterns in microarray gene expression profiles, and many clustering algorithms have been devised for the same. K-means is one of the popular algorithms for gene data clustering due to its simplicity and computational efficiency. But, K-means algorithm is highly sensitive to the choice of initial cluster centers. Thus, the algorithm easily gets trapped with local optimum if the initial centers are chosen randomly. This paper proposes a deterministic initialization algorithm for K-means (DK-means) by exploring a set of probable centers through a constrained bi-partitioning approach. The proposed algorithm is compared with classical K-means with random initialization and improved K-means variants such as K-means++ and MinMax algorithms. It is also compared with three deterministic initialization methods. Experimental analysis on gene expression datasets demonstrates that DK-means achieves improved results in terms of faster and stable convergence, and better cluster quality as compared to other algorithms.

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Authors and Affiliations

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

    R. Jothi

  2. Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur, Madhya Pradesh, India

    Sraban Kumar Mohanty & Aparajita Ojha

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  1. R. Jothi
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  2. Sraban Kumar Mohanty
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  3. Aparajita Ojha
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Correspondence to R. Jothi.

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Jothi, R., Mohanty, S.K. & Ojha, A. DK-means: a deterministic K-means clustering algorithm for gene expression analysis. Pattern Anal Applic 22, 649–667 (2019). https://doi.org/10.1007/s10044-017-0673-0

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  • DOI: https://doi.org/10.1007/s10044-017-0673-0

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