A robust method for clustering football players with mixed attributes
提出一种稳健的模糊聚类模型，能自动计算各属性权重并组合不同相似度矩阵，有效发现混合数据中的隐藏聚类结构，对足球运动员数据分析尤其有用。
Abstract A robust fuzzy clustering model for mixed data is proposed. For each variable, or attribute, a proper dissimilarity measure is computed and the clustering procedure combines the dissimilarity matrices with weights objectively computed during the optimization process. The weights reflect the relevance of each attribute type in the clustering results. A simulation study and an empirical application to football players data are presented that show the effectiveness of the proposed clustering algorithm in finding clusters that would be hidden unless a multi-attributes approach were used.