摘要
模糊聚类分析是非监督模式分类的一个分支,在模式识别中有着重要的地位。在FCM算法中,考虑到样本矢量中各维特征对模式分类的不同影响,本文引入一种基于特征加权的模糊聚类算法,该算法考虑了各维特征对分类的贡献不同,从而对数据进行了更有效的分类。
Fuzzy clustering analysis is a branch of unsupervised pattern classification, and plays an important role in fuzzy pattern recognition. In the Fuzzy c-Means algorithm, considering the particular contributions of different feature, a feature weight fuzzy clustering algorithm is introduced in this paper. By weighting the features of samples, better classification results can be achieved.
出处
《北京电子科技学院学报》
2007年第2期74-76,共3页
Journal of Beijing Electronic Science And Technology Institute
关键词
模糊聚类
FCM算法
特征加权
fuzzy clustering
FCM algorithm
feature weight
