A Low Dimensional Embedding Method for Combining Clusterings
Clustering combination has recently become a hotspot in machine learning, while its critical problem lies on how to combine multiple clusterings to yield a final superior result. In this paper, a low dimensional embedding method is proposed. It first obtains the low dimensional embeddings of hyperedges by performing spectral clustering algorithms and then obtains the low dimensional embeddings of objects indirectly by composition of mappings and finally performs K-means algorithm to cluster the objects according to their coordinates in the low dimensional space. Experimentally the proposed method is shown to perform well.
Daoguo Yang, Tianlong Gu, Huaiying Zhou, Jianmin Zeng and Zhengyi Jiang
S. Xu et al., "A Low Dimensional Embedding Method for Combining Clusterings", Advanced Materials Research, Vols. 201-203, pp. 2517-2520, 2011