A Low Dimensional Embedding Method for Combining Clusterings

Abstract:

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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.

Info:

Periodical:

Advanced Materials Research (Volumes 201-203)

Edited by:

Daoguo Yang, Tianlong Gu, Huaiying Zhou, Jianmin Zeng and Zhengyi Jiang

Pages:

2517-2520

DOI:

10.4028/www.scientific.net/AMR.201-203.2517

Citation:

S. Xu et al., "A Low Dimensional Embedding Method for Combining Clusterings", Advanced Materials Research, Vols. 201-203, pp. 2517-2520, 2011

Online since:

February 2011

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Price:

$35.00

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