改进的模糊C均值聚类算法

Improved fuzzy C-means clustering algorithm

把自适应的策略与传统的模糊C均值聚类算法结合起来,形成新的模糊聚类算法。在不影响收敛速度的情况下,它能够很好解决局部最优以及对初始值敏感的问题。以UCI机器学习数据库中的两组数据集为研究对象,实验结果表明,它的精确度与自适应免疫聚类算法相当,能够得到准确的簇的数目,并且它的收敛速度更快,这对于如今网络数据的高速变化来说,该方法显得更为重要。

Self-adaptive strategy with the traditional fuzzy C-means clustering algorithm forms a new fuzzy clustering algorithm. Without prejudice to the speed of convergence,it can resolve the problems of local optimal and sensitivity to initial values.With the two data sets in the database of UCI machine learning for the study,the experimental results indicate that it does not lose the precision to the adaptive immune clustering algorithm.The number of clusters is accurate and its faster convergence is more important in the nowadays of high-speed network data changing.