Paper Title:
A Low Dimensional Embedding Method for Combining Clusterings
  Abstract

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, T. Zhou, H. L. Yu, "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
$32.00
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