This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval
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Clustering Algorithms
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Cluster Analysis for Applications
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395
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Numerical Optimization of Computer Models
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298
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Cluster Analysis
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291
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Optimization of Control Parameters for Genetic Algorithms
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280
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A simplified neuron model as a principal component analyzer
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269
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Self-organization and associative
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206
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187
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169
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163
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An optimal graph theoretic approach to data clustering: theory and application to image segmentation
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158
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151
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150
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Graph-theoretical methods for detecting and describing Gestalt clusters
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147
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Pairwise data clustering by deterministic annealing
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146
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Developments in automatic text retrieval
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137
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Improved heterogeneous distance functions
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115
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Future paths for integer programming and links to artificial intelligence
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112
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Some methods for classification and analysis of multivariate observations
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96
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Artificial neural networks for feature extraction and multivariate data projection
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94
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Statistical inference for spatial processes
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94
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K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
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88
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86
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83
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A new approach to clustering
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81
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Goal-directed evaluation of binarization methods
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77
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Unsupervised texture segmentation in a deterministic annealing framework
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77
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Pattern Recognition: Human and Mechanical
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74
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73
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