BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
CSSIM '09 Proceedings of the 2009 International Conference on Computational Intelligence, Modelling and Simulation
Adaptive Population Differentiation PSO Algorithm
IITA '09 Proceedings of the 2009 Third International Symposium on Intelligent Information Technology Application - Volume 02
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Genetically Improved PSO Algorithm for Efficient Data Clustering
ICMLC '10 Proceedings of the 2010 Second International Conference on Machine Learning and Computing
An Improved Particle Swarm Optimization Algorithm with Synthetic Update Mechanism
IITSI '10 Proceedings of the 2010 Third International Symposium on Intelligent Information Technology and Security Informatics
Multi-dimensional particle swarm optimization in dynamic environments
Expert Systems with Applications: An International Journal
Particle swarm optimization with selective particle regeneration for data clustering
Expert Systems with Applications: An International Journal
An Intelligent Parameter Selection Method for Particle Swarm Optimization Algorithm
CSO '11 Proceedings of the 2011 Fourth International Joint Conference on Computational Sciences and Optimization
Review: A particle swarm optimization approach to clustering
Expert Systems with Applications: An International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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Clustering analysis is the task of assigning a set of objects to groups such that objects in one group or cluster are more similar to each other than to those in other clusters. Clustering analysis is the major application area of data mining where Particle Swarm Optimisation (PSO) is being widely implemented due to its simplicity and efficiency. When compared with techniques like K-means, Fuzzy C-means, K-Harmonic means and other traditional clustering approaches, in general, the PSO algorithm produces better results with reference to inter-cluster and intra-cluster distances, while having quantization errors comparable to the other algorithms. In recent times, many hybrid algorithms with PSO as one of the techniques have been developed to harness the strong points of PSO and increase its efficiency and accuracy. This paper provides an extensive review of the variants and hybrids of PSO which are being widely used for the purpose of clustering analysis.