A Self-Adaptive FCM for the Optimal Fuzzy Weighting Exponent
| Autor: | Min Ren, Zhihao Wang, Jirong Jiang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Degree (graph theory)
Computer science Fuzzy correlation Particle swarm optimization Self adaptive 02 engineering and technology 01 natural sciences Fuzzy logic Computer Science Applications Theoretical Computer Science Weighting ComputingMethodologies_PATTERNRECOGNITION 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Exponent 020201 artificial intelligence & image processing 010306 general physics Algorithm Software |
| Zdroj: | International Journal of Computational Intelligence and Applications. 18 |
| ISSN: | 1757-5885 1469-0268 |
| DOI: | 10.1142/s1469026819500081 |
| Popis: | Fuzzy weighting exponent [Formula: see text] is an important parameter of fuzzy [Formula: see text]-means (FCM), closely related to the performance of the algorithm. First, an improved fuzzy correlation degree was put forward to measure the relevance between the clusters, based on which a new cluster validity function was defined to evaluate the quality of the fuzzy partition. Then a self-adaptive FCM for the optimal value of [Formula: see text] was proposed with the aid of the global search ability of improved particle swarm algorithm to find out both the final clustering centroids and the optimal value of fuzzy weighting exponent automatically. The improved particle swarm algorithm updated the speed and the position based on dynamic inertia weight and learning factors, and introduced mutation of genetic algorithm to keep the diversity of the particles, preventing premature convergence. The experimental results showed that the proposed algorithm automatically calculated the optimal value of [Formula: see text] and meanwhile achieved better clustering results. |
| Databáze: | OpenAIRE |
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