In search of optimal clusters using genetic algorithms
Pattern Recognition Letters
A clustering algorithm using an evolutionary programming-based approach
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Machine Learning
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm
Neural Processing Letters
A new cluster validity measure and its application to image compression
Pattern Analysis & Applications
A kernel-based subtractive clustering method
Pattern Recognition Letters
Introduction to mathematical techniques in pattern recognition
Introduction to mathematical techniques in pattern recognition
Differential evolution and particle swarm optimisation in partitional clustering
Computational Statistics & Data Analysis
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
A fast divisive clustering algorithm using an improved discrete particle swarm optimizer
Pattern Recognition Letters
A survey: hybrid evolutionary algorithms for cluster analysis
Artificial Intelligence Review
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This paper introduces a method for clustering complex and linearly non-separable datasets, without any prior knowledge of the number of naturally occurring clusters. The proposed method is based on an improved variant of the Particle Swarm Optimization (PSO) algorithm. In addition, it employs a kernel-induced similarity measure instead of the conventional sum-of-squares distance. Use of the kernel function makes it possible to cluster data that is linearly non-separable in the original input space into homogeneous groups in a transformed high-dimensional feature space. Computer simulations have been undertaken with a test bench of five synthetic and three real life datasets, in order to compare the performance of the proposed method with a few state-of-the-art clustering algorithms. The results reflect the superiority of the proposed algorithm in terms of accuracy, convergence speed and robustness.