摘要
为进一步提高核模糊C-均值聚类算法的聚类性能,提出基于连续域混合蚁群优化的核模糊C-均值聚类算法(KFCM-HACO),使用HACO对KFCM算法的内核函数参数值和聚类中心进行优化,克服传统算法弊端,使核模糊C-均值聚类算法的目标函数最小化,加快算法的收敛速度.该优化算法在UCI数据集上的仿真实验及结果比较表明,KFCM-HACO算法的聚类性能优于传统的聚类算法,提高了聚类的准确性.
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.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2014年第9期841-846,共6页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.51008143)
江苏省高校自然科学研究项目(No.10JKB520006)资助
关键词
聚类分析
核模糊C-均值聚类
混合蚁群优化
连续概率密度函数
Clustering Analysis
Kernelized Fuzzy C-Means Clustering
Hybrid Ant Colony Optimization
Continuous Probability Density Function
