摘要
提出了一种基于遗传算法的动态模糊聚类方法.通过计算样本之间的模糊相似性,不失真地反映它们之间的内在关联.同时将样本之间的模糊相似性映射到样本之间的欧氏距离,即将高维样本映射到二维平面.利用遗传算法不断优化两者之间的映射,使样本之间的欧氏距离逐步趋近于其模糊相似性,实现动态模糊聚类.克服了聚类有效性对样本分布的依赖性;同时,增加了聚类的灵活性和可视化.该方法在性能上较经典的模糊聚类算法有一定改进,具有较好的聚类效果和较快的收敛速度.仿真实验结果证明了该方法的可行性和有效性.
A dynamic fuzzy clustering method is presented based on the genetic algorithm. By calculating the fuzzy similarity between samples the essential associations among samples are modeled factually. The fuzzy similarity between two samples is mapped into their Euclidean distance, that is, the high dimensional samples are mapped into the two dimensional plane. The mapping is optimized globally by the genetic algorithm, which adjusts the coordinates of each sample, and thus the Euclidean distance, to approximate to the fuzzy similarity between samples gradually. A key advantage of the proposed method is that the clustering is independent of the space distribution of input samples, which improves the flexibility and visualization. This method possesses characteristics of faster convergence rate and more exact clustering results than some typical clustering algorithms. Simulated experiments show the feasibility and availability of the proposed method.
出处
《北京邮电大学学报》
EI
CAS
CSCD
北大核心
2005年第1期75-78,共4页
Journal of Beijing University of Posts and Telecommunications
基金
国家自然科学基金项目(60175024)
教育部科学技术研究重点项目(02090)
关键词
动态模糊聚类
模糊相似矩阵
遗传算法
Convergence of numerical methods
Genetic algorithms
Mapping
Matrix algebra
Two dimensional
Visualization
