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Ensemble Clustering (EC), which seeks to generate a consensus clustering by integrating multiple base clusterings, has attracted increasing attentions. However, traditional EC methods typically have three main limitations: (1) High dimensional data present a huge challenge to ensemble clustering methods. (2) Most EC algorithms can not use prior information, e.g., pairwise constraints, to enhance the clustering performance. (3) Even in existing semi-supervised ensemble clustering methods, prior information is not sufficiently used, e.g., only used in generating base clusterings. To alleviate these problems, we propose Stratified Feature Sampling for Semi-Supervised Ensemble Clustering (SFS ³ EC). Firstly, we develop a novel stratified feature sampling method, which can cope with high dimensional data, guarantee the diversity of base clusterings, and reduce the risk that some features are not selected at the …