Fuzzy kernel clustering method based on improved Quantum-Behaved Particle Swarm Optimization algorithm
| Autor: | Duan Lian, Yuan Jingjing, Li Ling, Mai Xiongfa |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
Extrapolation Particle swarm optimization 02 engineering and technology Fuzzy logic Statistical classification Local optimum Kernel (image processing) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Cluster analysis Algorithm Test data |
| Zdroj: | 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). |
| DOI: | 10.1109/icccbda.2018.8386460 |
| Popis: | The fuzzy kernel clustering method (KFCM) is often sensitive to initial value and convergence to local optimum. In order to overcome these shortcomings, an improved Quantum-Behaved Particle Swarm Optimization (QPSO) algorithm was presented and used to optimize KFCM. In the improved QPSO which name as EQPSO (QPSO with Extrapolation), the particle take an extrapolation operator when it was worse than its previous generation particle in the evolution process. And then EQPSO-KFCM was put forward in which EQPSO is used to optimize the KFCM. The accuracy level of proposed algorithm EQPSO-KFCM was compared to those of KFCM and QPSO-KFCM on two test data set. The comparison indicates that EQPSO-KFCM clustering can be considered as a sufficiently accurate clustering method. In the end, EQPSO-KFCM is used in adaptability clustering of karst rocky desertification control patterns. |
| Databáze: | OpenAIRE |
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