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A sparse projection clustering algorithm

  • Published:
Journal of Electronics (China)

Abstract

A clustering algorithm based on Sparse Projection (SP), called Sparse Projection Clustering (SPC), is proposed in this letter. The basic idea is applying SP to project the observed data onto a high-dimensional sparse space, which is a nonlinear mapping with an explicit form and the K-means clustering algorithm can be therefore used to explore the inherent data patterns in the new space. The proposed algorithm is applied to cluster a complete artificial dataset and an incomplete real dataset. In comparison with the kernel K-means clustering algorithm, the proposed algorithm is more efficient.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China

    Zongbo Xie & Jiuchao Feng

Authors
  1. Zongbo Xie
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  2. Jiuchao Feng
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Corresponding author

Correspondence to Jiuchao Feng.

Additional information

Supported by the National Natural Science Foundation of China (No.60872123), the Joint Fund of the National Natural Science Foundation and the Guangdong Provincial Natural Science Foundation (No.U0835001).

Xie Zongbo, born in 19852, male, Ph.D. candidate.

Communication author: Feng Jiuchao, born in 1964, male, Professor.

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Xie, Z., Feng, J. A sparse projection clustering algorithm. J. Electron.(China) 26, 549–551 (2009). https://doi.org/10.1007/s11767-008-0135-3

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  • DOI: https://doi.org/10.1007/s11767-008-0135-3

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