Abstract：T-distributed Stochastic Neighbor Embedding (TSNE) is introduced into cluster ensemble problem and a cluster ensemble approach based on TSNE is proposed. First, TSNE is utilized to minimize Kullback-Leibler divergences between the high-dimensinal points corresponding to the rows of hypergraph’s adjacent matrix and the low-dimensional mapping points, which preserves the structure of high-dimensional space in low-dimensional space. Then, a hierarchical clustering algorithm is carried out in the low-dimensional space to obtain the final clustering result. Experimental results on several baseline datasets indicate that TSNE can improve the cluster results of hierarchical clustering algorithm and the proposed cluster ensemble method via TSNE outperforms state-of-the-art methods.
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