Algorithms for clustering data
Algorithms for clustering data
Simulated annealing: theory and applications
Simulated annealing: theory and applications
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
Journal of Global Optimization
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Using One-Class and Two-Class SVMs for Multiclass Image Annotation
IEEE Transactions on Knowledge and Data Engineering
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Advances in Fuzzy Clustering and its Applications
Advances in Fuzzy Clustering and its Applications
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Interactive Particle Swarm: A Pareto-Adaptive Metaheuristic to Multiobjective Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
A new multi-objective technique for differential fuzzy clustering
Applied Soft Computing
Unsupervised and Supervised Learning Approaches Together for Microarray Analysis
Fundamenta Informaticae
PMAFC: a new probabilistic memetic algorithm based fuzzy clustering
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Expert Systems with Applications: An International Journal
Fuzzy c-means improvement using relaxed constraints support vector machines
Applied Soft Computing
Rough set based fuzzy k-modes for categorical data
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
International Journal of Hybrid Intelligent Systems
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The problem of fuzzy clustering of categorical data, where no natural ordering among the elements of a categorical attribute domain can be found, is an important problem in exploratory data analysis. As a result, a few clustering algorithms with focus on categorical data have been proposed. In this paper, a modified differential evolution (DE)-based fuzzy c-medoids (FCMdd) clustering of categorical data has been proposed. The algorithm combines both local as well as global information with adaptive weighting. The performance of the proposed method has been compared with those using genetic algorithm, simulated annealing, and the classical DE technique, besides the FCMdd, fuzzy k-modes, and average linkage hierarchical clustering algorithm for four artificial and four real life categorical data sets. Statistical test has been carried out to establish the statistical significance of the proposed method. To improve the result further, the clustering method is integrated with a support vector machine (SVM), a well-known technique for supervised learning. A fraction of the data points selected from different clusters based on their proximity to the respective medoids is used for training the SVM. The clustering assignments of the remaining points are thereafter determined using the trained classifier. The superiority of the integrated clustering and supervised learning approach has been demonstrated.