Kernelized Fuzzy C-Means Clustering Algorithm Based on Hybrid Ant Colony Optimization for Continuous Domains
GUO Xiao-Fang1, LI Feng2, SONG Xiao-Ning1, WANG Wei-Dong1
1.School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003 2.School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003
Abstract：To further improve the clustering performance of kernelized fuzzy C-means clustering algorithm, a kernelized fuzzy C-means clustering algorithm based on hybrid ant colony optimization of continuous domain (KFCM-HACO) is proposed. Kernel function parameters value of KFCM algorithm is optimized by HACO, which overcomes the shortcomings of traditional algorithm, minimizes the objective function of kernelized fuzzy clustering algorithm, and speeds up the convergence rate of the algorithm. The simulation and comparison results on UCI dataset show that the KFCM-HACO algorithm outperforms the traditional clustering algorithm and improves the accuracy of clustering.
郭小芳，李锋，宋晓宁，王卫东. 基于连续域混合蚁群优化的核模糊C-均值聚类算法研究*[J]. 模式识别与人工智能, 2014, 27(9): 841-846.
GUO Xiao-Fang, LI Feng, SONG Xiao-Ning, WANG Wei-Dong. Kernelized Fuzzy C-Means Clustering Algorithm Based on Hybrid Ant Colony Optimization for Continuous Domains. , 2014, 27(9): 841-846.
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