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A novel clustering algorithm by clubbing GHFCM and GWO for microarray gene data

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Abstract

The advancement of data mining technology presents a way to examine and analyse the medical databases. Microarray data help in analysing the gene expressions, and the process of clustering helps in categorizing the data into organized groups. Grouping similar gene expressions paves the way for effective analysis, and the relationship between the expressions can be figured out. Recognizing the benefits of clustering, this work intends to present a clustering algorithm by combining generalized hierarchical fuzzy C means (GHFCM) and grey wolf optimization (GWO) algorithms. The GWO algorithm is utilized for selecting the initial clustering point, and the GHFCM algorithm is employed for clustering the microarray gene data. The performance of the proposed clustering algorithm is tested with respect to precision, recall, F-measure and time consumption, and the results are compared with the existing approaches. The performance of the proposed work is satisfactory with better F-measure rates and minimal time consumption.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jayaraj Annapackiam CSI College of Engineering, Nazareth, India

    P. Edwin Dhas

  2. Department of Electronics and Instrumentation Engineering, National Engineering College, Kovilpatti, India

    B. Sankara Gomathi

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  1. P. Edwin Dhas
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  2. B. Sankara Gomathi
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Correspondence to P. Edwin Dhas.

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Edwin Dhas, P., Sankara Gomathi, B. A novel clustering algorithm by clubbing GHFCM and GWO for microarray gene data. J Supercomput 76, 5679–5693 (2020). https://doi.org/10.1007/s11227-019-02953-z

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  • DOI: https://doi.org/10.1007/s11227-019-02953-z

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