Fuzzy Clustering Algorithm with Ranking Features and Identifying Noise Simultaneously
具有特征排序功能的鲁棒性模糊聚类方法

This paper proposes a weighted fuzzy clustering algorithm (FCA) that can identify the noise of samples and rank the samples' features simultaneously according to their contribution degrees, while realizing clustering efficiently. Therefore, the FCA is robust and can be used to extract the sample's features. Experimental results indicate the above advantages of the FCA.