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GACC: genetic algorithm-based categorical data clustering for large datasets

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  • Abha Sharma
  • R.S. Thakur

Abstract

Many operators of genetic algorithm (GA) are discussed in the literature such as crossover operators, fitness functions, mutation, etc. A range of GA-based clustering methods have been proposed to obtain optimal solutions. In this paper, most recent GA-based hard and fuzzy clustering which is specifically designed for categorical data is discussed. In general, all GA-based clustering algorithms generate the initial population randomly, which may produce biased results. This paper proposed GACC algorithm with new population initialisation criteria. In this population creation mechanism, the usual random selection of chromosomes is replaced with more refined and distinct clusters as chromosomes. This mechanism prohibits the user to initialise the population size as well. Experimental results show the better clustering for the pure categorical dataset. The work finishes off with some open challenges and ways to improve clustering of categorical data.

Suggested Citation

  • Abha Sharma & R.S. Thakur, 2017. "GACC: genetic algorithm-based categorical data clustering for large datasets," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 9(4), pages 275-297.
    • Handle: RePEc:ids:ijdmmm:v:9:y:2017:i:4:p:275-297
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    Cited by:

    1. Abha Sharma & Pushpendra Kumar & Kanojia Sindhuben Babulal & Ahmed J. Obaid & Harshita Patel, 2022. "Categorical Data Clustering Using Harmony Search Algorithm for Healthcare Datasets," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 13(4), pages 1-15, August.

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