Alternatives to the k-means algorithm that find better clusterings
Proceedings of the eleventh international conference on Information and knowledge management
On the performance of ant-based clustering
Design and application of hybrid intelligent systems
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
Accurate integration of multi-view range images using k-means clustering
Pattern Recognition
Text document clustering based on frequent word meaning sequences
Data & Knowledge Engineering
Expert Systems with Applications: An International Journal
A hybridized approach to data clustering
Expert Systems with Applications: An International Journal
A heuristic-based fuzzy co-clustering algorithm for categorization of high-dimensional data
Fuzzy Sets and Systems
Fuzzy clustering to detect tuberculous meningitis-associated hyperdensity in CT images
Computers in Biology and Medicine
An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers
Computers and Operations Research
Techniques for clustering gene expression data
Computers in Biology and Medicine
Towards effective document clustering: A constrained K-means based approach
Information Processing and Management: an International Journal
Adaptation of the F-measure to cluster based lexicon quality evaluation
Evalinitiatives '03 Proceedings of the EACL 2003 Workshop on Evaluation Initiatives in Natural Language Processing: are evaluation methods, metrics and resources reusable?
Computational Statistics & Data Analysis
A new clustering algorithm based on PSO with the jumping mechanism of SA
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Fuzzy C-means and fuzzy swarm for fuzzy clustering problem
Expert Systems with Applications: An International Journal
A novel hybrid K-harmonic means and gravitational search algorithm approach for clustering
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Data clustering based on an efficient hybrid of K-harmonic means, PSO and GA
Transactions on computational collective intelligence IV
A survey: hybrid evolutionary algorithms for cluster analysis
Artificial Intelligence Review
Engineering Applications of Artificial Intelligence
A new grouping genetic algorithm for clustering problems
Expert Systems with Applications: An International Journal
A hybrid network intrusion detection system using simplified swarm optimization (SSO)
Applied Soft Computing
Particle swarm optimization with increasing topology connectivity
Engineering Applications of Artificial Intelligence
Integrated Computer-Aided Engineering
| Hi-index | 12.06 |
Clustering is the process of grouping data objects into set of disjoint classes called clusters so that objects within a class are highly similar with one another and dissimilar with the objects in other classes. K-means (KM) algorithm is one of the most popular clustering techniques because it is easy to implement and works fast in most situations. However, it is sensitive to initialization and is easily trapped in local optima. K-harmonic means (KHM) clustering solves the problem of initialization using a built-in boosting function, but it also easily runs into local optima. Particle Swarm Optimization (PSO) algorithm is a stochastic global optimization technique. A hybrid data clustering algorithm based on PSO and KHM (PSOKHM) is proposed in this research, which makes full use of the merits of both algorithms. The PSOKHM algorithm not only helps the KHM clustering escape from local optima but also overcomes the shortcoming of the slow convergence speed of the PSO algorithm. The performance of the PSOKHM algorithm is compared with those of the PSO and the KHM clustering on seven data sets. Experimental results indicate the superiority of the PSOKHM algorithm.