Algorithms for clustering data
Algorithms for clustering data
A hybridized approach to data clustering
Expert Systems with Applications: An International Journal
Data mining with a simulated annealing based fuzzy classification system
Pattern Recognition
Algorithm Note: PK-means: A new algorithm for gene clustering
Computational Biology and Chemistry
A new arrhythmia clustering technique based on Ant Colony Optimization
Journal of Biomedical Informatics
K-means Optimization Algorithm for Solving Clustering Problem
WKDD '09 Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining
An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization
Expert Systems with Applications: An International Journal
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A new clustering algorithm is proposed based on particle swarm optimization (PSO). The main idea of the new algorithm is to solve clustering problem using the fast search ability of the particle swarm optimization, each particle is composed of a cluster center vector, and represents a possible solution of the clustering problem. To escape from local optimum, a new idea is proposed, that is the neighborhood structure af individual optimum is enriched using the probabilistic jumping property of the simulated annealing (SA). The individual optimum of the particles is disturbed randomly, that is the data pattern clustering label is changed randomly, so the search ability of the global space is enhanced. The experimental results on different datasets show that the new algorithm has better performance than particle swarm optimization and K-means algorithm, has better global convergence, and it is an effective clustering algorithm.