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An enhanced genetic algorithm with new mutation for cluster analysis

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Abstract

This paper proposed a new methodology to perform cluster analysis based on genetic algorithm (GA). Firstly, the population of GA is initialized by k-means algorithm to reach the best centers of clusters. Secondly, the GA operators are applied. New mutation is proposed depending on the extreme points in clusters groups to overcome the limitations of k-means algorithm. Finally, the proposed approach is applied on a set of data consists of a non-overlapping data and large datasets with high dimensionality from machine learning repository (UCI). In addition an electrical application is used to measure the capability of our approach to solve real world application. The results proved the superiority of the new methodology.

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Acknowledgements

The authors are grateful to the anonymous reviewers for their valuable comments and helpful suggestions which greatly improved the paper’s quality.

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Authors and Affiliations

  1. Department of Mathematics, College of Science and Humanities Studies, Prince Sattam Bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia

    M. A. El-Shorbagy

  2. Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Shebin El-Kom, Egypt

    M. A. El-Shorbagy, A. Y. Ayoub, A. A. Mousa & I. M. El-Desoky

  3. Mathematics and Statistics Department, Faculty of Science, Taif University, Taif, Kingdom of Saudi Arabia

    A. A. Mousa

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  1. M. A. El-Shorbagy
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  2. A. Y. Ayoub
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  3. A. A. Mousa
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  4. I. M. El-Desoky
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Correspondence to M. A. El-Shorbagy.

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El-Shorbagy, M.A., Ayoub, A.Y., Mousa, A.A. et al. An enhanced genetic algorithm with new mutation for cluster analysis. Comput Stat 34, 1355–1392 (2019). https://doi.org/10.1007/s00180-019-00871-5

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