Competitive learning algorithms for vector quantization
Neural Networks
Bayesian Ying-Yang machine, clustering and number of clusters
Pattern Recognition Letters - special issue on pattern recognition in practice V
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
IEEE Transactions on Knowledge and Data Engineering
A method for initialising the K-means clustering algorithm using kd-trees
Pattern Recognition Letters
Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques
Journal of Classification
A genetic algorithm that exchanges neighboring centers for k-means clustering
Pattern Recognition Letters
The mahalanobis distance based rival penalized competitive learning algorithm
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Rival penalized competitive learning for clustering analysis, RBF net, and curve detection
IEEE Transactions on Neural Networks
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis
Applied Soft Computing
Validity index for clusters of different sizes and densities
Pattern Recognition Letters
Engineering Applications of Artificial Intelligence
A review on particle swarm optimization algorithms and their applications to data clustering
Artificial Intelligence Review
AHSPeR: adaptive hypermedia system oriented toward personalization of readings plans
AICT'11 Proceedings of the 2nd international conference on Applied informatics and computing theory
An architecture for component-based design of representative-based clustering algorithms
Data & Knowledge Engineering
Energy supply network design optimization for distributed energy systems
Computers and Industrial Engineering
k'-Means algorithms for clustering analysis with frequency sensitive discrepancy metrics
Pattern Recognition Letters
A fast partitioning algorithm and its application to earthquake investigation
Computers & Geosciences
Kernel k'-means algorithm for clustering analysis
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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This paper introduces k'-means algorithm that performs correct clustering without pre-assigning the exact number of clusters. This is achieved by minimizing a suggested cost-function. The cost-function extends the mean-square-error cost-function of k-means. The algorithm consists of two separate steps. The first is a pre-processing procedure that performs initial clustering and assigns at least one seed point to each cluster. During the second step, the seed-points are adjusted to minimize the cost-function. The algorithm automatically penalizes any possible winning chances for all rival seed-points in subsequent iterations. When the cost-function reaches a global minimum, the correct number of clusters is determined and the remaining seed points are located near the centres of actual clusters. The simulated experiments described in this paper confirm good performance of the proposed algorithm.