一种基于属性加权模糊聚类算法

A New Fuzzy C-means Based on Variable Weight

针对传统的模糊聚类算法(FCM)的不足,提出了具体的改进和提高方法,通过修改聚类目标函数来提高算法处理噪音点的能力和体现样本空间各维度对聚类效果的价值。最后通过实验比较证明了算法的有效性。

As a traditional fuzzy C-means（FCM） has its shortages, we present an improved algorithm： FCM_BVW.By improving the clustering objective function,the abilities of C-means to handle noise and to show the significance of each dimension in the samples for clustering effect are enhanced.And lastly,there is an experiment which makes the new FCM clearer.