An enhanced genetic algorithm with new mutation for cluster analysis

M. A. El-Shorbagy (), A. Y. Ayoub, A. A. Mousa and I. M. El-Desoky
Additional contact information
M. A. El-Shorbagy: Prince Sattam Bin Abdulaziz University
A. Y. Ayoub: Menoufia University
A. A. Mousa: Menoufia University
I. M. El-Desoky: Menoufia University

Computational Statistics, 2019, vol. 34, issue 3, No 18, 1355-1392

Abstract: 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.

Keywords: Cluster analysis; k-means algorithm; Datasets; Evolutionary methods (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00180-019-00871-5

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