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Genetic algorithm-based clustering technique
A genetic algorithm-based clustering technique, called GA-clustering, is proposed in this article. Expand
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Performance Evaluation of Some Clustering Algorithms and Validity Indices
In this article, we evaluate the performance of three clustering algorithms, hard K-Means, single linkage, and a simulated annealing (SA) based clustering technique, in conjunction with four cluster validity indices, namely Davies-Bouldin index, Dunn's index, Calinski-Harabasz index and a recently developed index I. Expand
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Validity index for crisp and fuzzy clusters
A cluster validity index and its fuzzification is described, which can provide a measure of goodness of clustering on different partitions of a data set. Expand
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A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
This paper describes a simulated annealing based multiobjective optimization algorithm that incorporates the concept of archive in order to provide a set of tradeoff solutions for the problem under consideration. Expand
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Genetic clustering for automatic evolution of clusters and application to image classification
A new string representation, comprising both real numbers and the do not care symbol, is used in order to encode a variable number of clusters. Expand
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Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients
In this article we describe a novel Particle Swarm Optimization (PSO) approach to multi-objective optimization (MOO), which is made adaptive in nature by allowing its vital parameters (viz., inertia weight and acceleration coefficients) to change with iterations. Expand
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Nonparametric genetic clustering: comparison of validity indices
A variable-string-length genetic algorithm (GA) is used for developing a novel nonparametric clustering technique when the number of clusters is not fixed a-priori. Chromosomes in the same populationExpand
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An evolutionary technique based on K-Means algorithm for optimal clustering in RN
A genetic algorithm-based efficient clustering technique that utilizes the principles of K-Means algorithm and avoids its major limitation of getting stuck at locally optimal values. Expand
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Fuzzy partitioning using a real-coded variable-length genetic algorithm for pixel classification
The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. Expand
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A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification
In this article, the effectiveness of variable string length genetic algorithm along with a recently developed fuzzy cluster validity index (PBMF) has been demonstrated for clustering a data set into an unknown number of clusters. Expand
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