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Genetic algorithm-based clustering technique
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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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