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改进的模糊C-均值聚类算法
引用本文:关庆,邓赵红,王士同.改进的模糊C-均值聚类算法[J].计算机工程与应用,2011,47(10):27-29.
作者姓名:关庆  邓赵红  王士同
作者单位:江南大学信息工程学院,江苏,无锡,214122
基金项目:国家自然科学基金,江苏省自然科学基金
摘    要:为了克服模糊C-均值(FCM)聚类算法易陷入局部极小值和对初始值敏感的缺点,提出了一种基于改进量子蚁群的模糊聚类算法。将量子计算原理和蚁群算法相结合来改进FCM算法。初期采用量子遗传算法生成信息素分布,后期利用蚁群算法的全局搜索性、并行计算性等特点避免聚类陷入局部最优解。实验证明该算法保证了种群的多样性,有较好的全局收敛性,克服了模糊C-均值聚类算法的不足,能有效解决未成熟收敛的问题,使聚类问题最终快速、有效地收敛到全局最优解。

关 键 词:聚类分析  模糊C-均值聚类  蚁群算法  量子计算
修稿时间: 

Improved fuzzy C-means clustering algorithm
GUAN Qing,DENG Zhaohong,WANG Shitong.Improved fuzzy C-means clustering algorithm[J].Computer Engineering and Applications,2011,47(10):27-29.
Authors:GUAN Qing  DENG Zhaohong  WANG Shitong
Affiliation:School of Information Technology,Jiangnan University,Wuxi,Jiangsu 214122,China
Abstract:In order to overcome the Fuzzy C-Means(FCM) clustering algorithm falling into local minimum value and the shortcomings of the initial value of sensitivity,a improved ant colony algorithm based on quantum for fuzzy clustering is proposed.Quantum computing will combine theory and ant colony algorithm to improve the FCM algorithm.It uses quantum genetic algorithm to generate the initial pheromone distribution,and then updates using quantum gates quantum ants carry bits;later uses the ant colony algorithm for global search,parallel computing cluster and other characteristics to avoid falling into local optimal solution.The algorithm is verified to ensure the diversity of population,have a good global convergence and overcome the fuzzy C-means clustering algorithm deficiencies,and can effectively solve the premature convergence problem,so that the final clustering problem quickly and efficiently converges to the global optimal solution.
Keywords:cluster analysis  Fuzzy C-Means(FCM)clustering  ant colony algorithm  quantum computing
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