An enhanced genetic algorithm with new mutation for cluster analysis.

Autor:	El-Shorbagy, M. A., Ayoub, A. Y., Mousa, A. A., El-Desoky, I. M.
Předmět:	GENETIC algorithms K-means clustering POINT set theory MACHINE learning
Zdroj:	Computational Statistics; Sep2019, Vol. 34 Issue 3, p1355-1392, 38p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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