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Data clustering using K-Means based on Crow Search Algorithm

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Abstract

Cluster analysis is one of the popular data mining techniques and it is defined as the process of grouping similar data. K-Means is one of the clustering algorithms to cluster the numerical data. The features of K-Means clustering algorithm are easy to implement and it is efficient to handle large amounts of data. The major problem with K-Means is the selection of initial centroids. It selects the initial centroids randomly and it leads to a local optimum solution. Recently, nature-inspired optimization algorithms are combined with clustering algorithms to obtain the global optimum solution. Crow Search Algorithm (CSA) is a new population-based metaheuristic optimization algorithm. This algorithm is based on the intelligent behaviour of the crows. In this paper, CSA is combined with the K-Means clustering algorithm to obtain the global optimum solution. Experiments are conducted on benchmark datasets and the results are compared to those from various clustering algorithms and optimization-based clustering algorithms. Also the results are evaluated with internal, external and statistical experiments to prove the efficiency of the proposed algorithm.

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Authors and Affiliations

  1. Department of Computer Applications, Kongu Engineering College, Perundurai, India

    K Lakshmi & S Shanthi

  2. Department of Computer Science, Government Arts and Science College, Kangeyam, India

    N Karthikeyani Visalakshi

Authors
  1. K Lakshmi
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  2. N Karthikeyani Visalakshi
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  3. S Shanthi
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Correspondence to K Lakshmi.

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Lakshmi, K., Visalakshi, N.K. & Shanthi, S. Data clustering using K-Means based on Crow Search Algorithm. Sādhanā 43, 190 (2018). https://doi.org/10.1007/s12046-018-0962-3

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  • DOI: https://doi.org/10.1007/s12046-018-0962-3

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