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
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
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
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
Applying K-harmonic means clustering to the part-machine classification problem
Expert Systems with Applications: An International Journal
An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization
Expert Systems with Applications: An International Journal
GSA: A Gravitational Search Algorithm
Information Sciences: 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
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ICE - Intelligent Clustering Engine: A clustering gadget for Google Desktop
Expert Systems with Applications: An International Journal
A novel chemistry based metaheuristic optimization method for mining of classification rules
Expert Systems with Applications: An International Journal
A discrete gravitational search algorithm for solving combinatorial optimization problems
Information Sciences: an International Journal
Hi-index | 12.06 |
Clustering is used to group data objects into sets of disjoint classes called clusters so that objects within the same class are highly similar to each other and dissimilar from the objects in other classes. K-harmonic means (KHM) is one of the most popular clustering techniques, and has been applied widely and works well in many fields. But this method usually runs into local optima easily. A hybrid data clustering algorithm based on an improved version of Gravitational Search Algorithm and KHM, called IGSAKHM, is proposed in this research. With merits of both algorithms, IGSAKHM not only helps the KHM clustering to escape from local optima but also overcomes the slow convergence speed of the IGSA. The proposed method is compared with some existing algorithms on seven data sets, and the obtained results indicate that IGSAKHM is superior to KHM and PSOKHM in most cases.