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Automatic clustering and feature selection using gravitational search algorithm and its application to microarray data analysis

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Abstract

This paper proposes a novel approach that selects the number of clusters along with relevant features automatically and simultaneously. Gravitational search algorithm is used as metaheuristic. A novel agent representation scheme is used for encoding cluster centers and number of features. The algorithm is able to find the optimal number of clusters and the relevant features corresponding to the clusters during the run time. A new concept of threshold setting is used. The variance (statistical property) of the dataset has been exploited. To make the search efficient, a novel clustering criterion is used. The proposed approach is compared with recently developed well-known clustering techniques. This approach is further applied for analysis of microarray data. The statistical and biological significance tests are performed to demonstrate the efficiency of proposed approach. The results prove the effectiveness and the accuracy of the proposed algorithm.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Thapar University, Patiala, Punjab, India

    Vijay Kumar

  2. Computer Science and Engineering Department, GJUS&T, Hisar, Haryana, India

    Dinesh Kumar

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  1. Vijay Kumar
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  2. Dinesh Kumar
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Correspondence to Vijay Kumar.

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Kumar, V., Kumar, D. Automatic clustering and feature selection using gravitational search algorithm and its application to microarray data analysis. Neural Comput & Applic 31, 3647–3663 (2019). https://doi.org/10.1007/s00521-017-3321-0

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  • DOI: https://doi.org/10.1007/s00521-017-3321-0

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