A clustering algorithm using an evolutionary programming-based approach

doi:10.1016/S0167-8655(97)00122-0

Pattern Recognition Letters

Volume 18, Issue 10, October 1997, Pages 975-986

https://doi.org/10.1016/S0167-8655(97)00122-0 Get rights and content

Abstract

In this paper, an evolutionary programming-based clustering algorithm is proposed. The algorithm effectively groups a given set of data into an optimum number of clusters. The proposed method is applicable for clustering tasks where clusters are crisp and spherical. This algorithm determines the number of clusters and the cluster centers in such a way that locally optimal solutions are avoided. The result of the algorithm does not depend critically on the choice of the initial cluster centers.

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View full text

A clustering algorithm using an evolutionary programming-based approach

Abstract

Pattern Recognition

An evolutionary algorithm that constructs recurrent neural networks

IEEE Trans. Neural Networks

Pattern Recognition with Fuzzy Objective Function Algorithms

Algorithms that Cluster Data

Asymptotic convergence properties of genetic algorithms and evolutionary programming

Cybernetics and Systems

An introduction to simulated evolutionary optimization

IEEE Trans. Neural Networks

Evolutionary Computation: Toward a New Philosophy of Machine Learning

Unsupervised optimal fuzzy clustering

IEEE Trans. Pattern Anal. Machine Intell.

Genetic Algorithms in Search, Optimization and Machine Learning