计算机工程与应用2011，Vol.47Issue(10)：27-29,88,4.DOI:10.3778/j.issn.1002-8331.2011.10.008
改进的模糊C-均值聚类算法
Improved fuzzy C-means clustering algorithm
摘要
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 intial 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.关键词
聚类分析/模糊C-均值聚类/蚁群算法/量子计算Key words
cluster analysis/Fuzzy C-Means(FCM) clustering/ant colony algorithm/quantum computing分类
信息技术与安全科学引用本文复制引用
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):27-29,88,4.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60903100) （the National Natural Science Foundation of China under Grant No.60903100）
江苏省自然科学基金(No.BK2009067). （No.BK2009067）
