Algorithms for clustering data
Algorithms for clustering data
The handbook of brain theory and neural networks
ACM Computing Surveys (CSUR)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
A new initialization method for categorical data clustering
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A boltzmann theory based dynamic agglomerative hierarchical clustering
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Fuzzy C-means and fuzzy swarm for fuzzy clustering problem
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Adjusting the clustering results referencing an external set
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Data clustering using modified fuzzy-PSO (MFPSO)
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Attribute value weighting in k-modes clustering
Expert Systems with Applications: An International Journal
A bio inspired fuzzy k-modes clustring algorithm
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Rough set based fuzzy k-modes for categorical data
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
A powerful hybrid clustering method based on modified stem cells and Fuzzy C-means algorithms
Engineering Applications of Artificial Intelligence
International Journal of Hybrid Intelligent Systems
Hi-index | 12.06 |
The fuzzy k-Modes algorithm introduced by Huang and Ng [Huang, Z., & Ng, M. (1999). A fuzzy k-modes algorithm for clustering categorical data. IEEE Transactions on Fuzzy Systems, 7(4), 446-452] is very effective for identifying cluster structures from categorical data sets. However, the algorithm may stop at locally optimal solutions. In order to search for appropriate fuzzy membership matrices which can minimize the fuzzy objective function, we present a hybrid genetic fuzzy k-Modes algorithm in this paper. To circumvent the expensive crossover operator in genetic algorithms (GAs), we hybridize GA with the fuzzy k-Modes algorithm and define the crossover operator as a one-step fuzzy k-Modes algorithm. Experiments on two real data sets are carried out to illustrate the performance of the proposed algorithm.