The fuzzy sets and systems based on AFS.structure, EI algebra and EII algebra
Fuzzy Sets and Systems
A new fuzzy model of pattern recognition and hitch diagnoses of complex systems
Fuzzy Sets and Systems
Geometric Data Analysis: An Empirical Approach to Dimensionality Reduction and the Study of Patterns
Geometric Data Analysis: An Empirical Approach to Dimensionality Reduction and the Study of Patterns
The Cluster Dissection and Analysis Theory FORTRAN Programs Examples
The Cluster Dissection and Analysis Theory FORTRAN Programs Examples
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Feature selection based on a modified fuzzy C-means algorithm with supervision
Information Sciences—Informatics and Computer Science: An International Journal
The Journal of Machine Learning Research
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Sciences—Informatics and Computer Science: An International Journal
Feature selection in robust clustering based on Laplace mixture
Pattern Recognition Letters
Information cut for clustering using a gradient descent approach
Pattern Recognition
The development of fuzzy decision trees in the framework of Axiomatic Fuzzy Set logic
Applied Soft Computing
Information Sciences: an International Journal
A robust deterministic annealing algorithm for data clustering
Data & Knowledge Engineering
Localized feature selection for clustering
Pattern Recognition Letters
Concept analysis via rough set and AFS algebra
Information Sciences: an International Journal
A distributed PSO-SVM hybrid system with feature selection and parameter optimization
Applied Soft Computing
Two novel feature selection approaches for web page classification
Expert Systems with Applications: An International Journal
The Development of Fuzzy Rough Sets with the Use of Structures and Algebras of Axiomatic Fuzzy Sets
IEEE Transactions on Knowledge and Data Engineering
A Novel Feature Selection Methodology for Automated Inspection Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised fuzzy clustering: A kernel-based approach
Knowledge-Based Systems
Axiomatic Fuzzy Set Theory and Its Applications
Axiomatic Fuzzy Set Theory and Its Applications
A new Kernelized hybrid c-mean clustering model with optimized parameters
Applied Soft Computing
The fuzzy clustering algorithm based on AFS topology
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Credit rating analysis with AFS fuzzy logic
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
The fuzzy clustering analysis based on AFS theory
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Wavelet Feature Selection for Image Classification
IEEE Transactions on Image Processing
General fuzzy min-max neural network for clustering and classification
IEEE Transactions on Neural Networks
A parsimony fuzzy rule-based classifier using axiomatic fuzzy set theory and support vector machines
Information Sciences: an International Journal
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Artificial intelligence is the study of how computer systems can simulate intelligent processes such as learning, reasoning, and understanding symbolic information in context. Axiomatic Fuzzy Set (AFS) theory, in which fuzzy sets (membership functions) and their logic operations are determined by a consistent algorithm according to the distributions of original data and the semantics of the fuzzy concepts, is applied to study some new techniques of feature selection, concept categorization and characteristic description; problems often encountered in artificial intelligence area such as machine learning and pattern recognition. These techniques developed under the framework of AFS theory in this paper are more simple and more interpretable than the conventional methods, since they imitate the human recognition process. In order to evaluate the effectiveness of the feature selection, the concept categorization and the characteristic description, these new techniques are applied to fuzzy clustering problems. Several benchmark data sets are used for this purpose. Clustering accuracies are comparable with or superior to the conventional algorithms such as FCM, k-means, and the new algorithm such as single point iterative weighted fuzzy C-means clustering algorithm.