A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Algorithms for clustering data
Algorithms for clustering data
The 'Neural' Phonetic Typewriter
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Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Introduction to the theory of neural computation
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Adaptation in natural and artificial systems
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BIRCH: an efficient data clustering method for very large databases
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ACM Computing Surveys (CSUR)
An Algorithm for Finding Best Matches in Logarithmic Expected Time
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Optimal Expected-Time Algorithms for Closest Point Problems
ACM Transactions on Mathematical Software (TOMS)
A clustering algorithm based on graph connectivity
Information Processing Letters
Stochastic K-means algorithm for vector quantization
Pattern Recognition Letters
A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection and Separation of Ring-Shaped Clusters Using Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A Modified K-Means Algorithm for Circular Invariant Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Fuzzy Systems
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IEEE Transactions on Fuzzy Systems
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
Some Symmetry Based Classifiers
Fundamenta Informaticae
A genetic algorithm with gene rearrangement for K-means clustering
Pattern Recognition
A new point symmetry based fuzzy genetic clustering technique for automatic evolution of clusters
Information Sciences: an International Journal
A new multiobjective simulated annealing based clustering technique using symmetry
Pattern Recognition Letters
Performance evaluation of some symmetry-based cluster validity indexes
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Semi-GAPS: A Semi-supervised Clustering Method Using Point Symmetry
Fundamenta Informaticae
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ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
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Fundamenta Informaticae
A new multiobjective clustering technique based on the concepts of stability and symmetry
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Quantization-based clustering algorithm
Pattern Recognition
A clonal selection clustering algorithm using pointed symmetry-based distance measure
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A new multi-objective technique for differential fuzzy clustering
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Expert Systems with Applications: An International Journal
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Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Pattern Recognition
Some connectivity based cluster validity indices
Applied Soft Computing
Gene transposon based clone selection algorithm for automatic clustering
Information Sciences: an International Journal
Some Symmetry Based Classifiers
Fundamenta Informaticae
MR Brain Image Segmentation Using A Multi-seed Based Automatic Clustering Technique
Fundamenta Informaticae
A generalized automatic clustering algorithm in a multiobjective framework
Applied Soft Computing
Expert Systems with Applications: An International Journal
Gene expression data clustering using a multiobjective symmetry based clustering technique
Computers in Biology and Medicine
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In this paper, an evolutionary clustering technique is described that uses a new point symmetry-based distance measure. The algorithm is therefore able to detect both convex and non-convex clusters. Kd-tree based nearest neighbor search is used to reduce the complexity of finding the closest symmetric point. Adaptive mutation and crossover probabilities are used. The proposed GA with point symmetry (GAPS) distance based clustering algorithm is able to detect any type of clusters, irrespective of their geometrical shape and overlapping nature, as long as they possess the characteristic of symmetry. GAPS is compared with existing symmetry-based clustering technique SBKM, its modified version, and the well-known K-means algorithm. Sixteen data sets with widely varying characteristics are used to demonstrate its superiority. For real-life data sets, ANOVA and MANOVA statistical analyses are performed.