A cluster validity index for fuzzy clustering
Information Sciences: an International Journal
Concept analysis via rough set and AFS algebra
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
The fuzzy clustering algorithm based on AFS topology
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Nearness approximation space based on axiomatic fuzzy sets
International Journal of Approximate Reasoning
A parsimony fuzzy rule-based classifier using axiomatic fuzzy set theory and support vector machines
Information Sciences: an International Journal
Supplier selection using axiomatic fuzzy set and TOPSIS methodology in supply chain management
Fuzzy Optimization and Decision Making
Development of Near Sets Within the Framework of Axiomatic Fuzzy Sets
Fundamenta Informaticae
Applications of axiomatic fuzzy sets theory on fuzzy time series forecasting
International Journal of Systems, Control and Communications
Extraction of fuzzy rules from fuzzy decision trees: An axiomatic fuzzy sets (AFS) approach
Data & Knowledge Engineering
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
In the framework of axiomatic fuzzy sets theory, we first study how to impersonally and automatically determine the membership functions for fuzzy sets according to original data and facts, and a new algorithmic framework of determining membership functions and their logic operations for fuzzy sets has been proposed. Then, we apply the proposed algorithmic framework to give a new clustering algorithm and show that the algorithm is feasible. A number of illustrative examples show that this approach offers a far more flexible and effective means for the intelligent systems in real-world applications. Compared with popular fuzzy clustering algorithms, such as c-means fuzzy algorithm and k-nearest-neighbor fuzzy algorithm, the new fuzzy clustering algorithm is more simple and understandable, the data types of the attributes can be various data types or subpreference relations, even descriptions of human intuition, and the distance function and the class number need not be given beforehand.