Reducing data dimensionality using random projections and fuzzy k ‐means clustering

Reducing data dimensionality using random projections and fuzzy k ‐means clustering Purpose – The purpose of this paper is to introduce a new hybrid method for reducing dimensionality of high dimensional data. Design/methodology/approach – Literature on dimensionality reduction (DR) witnesses the research efforts that combine random projections (RP) and singular value decomposition (SVD) so as to derive the benefit of both of these methods. However, SVD is well known for its computational complexity. Clustering under the notion of concept decomposition is proved to be less computationally complex than SVD and useful for DR. The method proposed in this paper combines RP and fuzzy k ‐means clustering (FKM) for reducing dimensionality of the data. Findings – The proposed RP‐FKM is computationally less complex than SVD, RP‐SVD. On the image data, the proposed RP‐FKM has produced less amount of distortion when compared with RP. The proposed RP‐FKM provides better text retrieval results when compared with conventional RP and performs similar to RP‐SVD. For the text retrieval task, superiority of SVD over other DR methods noted here is in good agreement with the analysis reported by Moravec. Originality/value – The hybrid method proposed in this paper, combining RP and FKM, is new. Experimental results indicate that the proposed method is useful for reducing dimensionality of high‐dimensional data such as images, text, etc. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Intelligent Computing and Cybernetics Emerald Publishing

Reducing data dimensionality using random projections and fuzzy k ‐means clustering

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Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
1756-378X
DOI
10.1108/17563781111160020
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to introduce a new hybrid method for reducing dimensionality of high dimensional data. Design/methodology/approach – Literature on dimensionality reduction (DR) witnesses the research efforts that combine random projections (RP) and singular value decomposition (SVD) so as to derive the benefit of both of these methods. However, SVD is well known for its computational complexity. Clustering under the notion of concept decomposition is proved to be less computationally complex than SVD and useful for DR. The method proposed in this paper combines RP and fuzzy k ‐means clustering (FKM) for reducing dimensionality of the data. Findings – The proposed RP‐FKM is computationally less complex than SVD, RP‐SVD. On the image data, the proposed RP‐FKM has produced less amount of distortion when compared with RP. The proposed RP‐FKM provides better text retrieval results when compared with conventional RP and performs similar to RP‐SVD. For the text retrieval task, superiority of SVD over other DR methods noted here is in good agreement with the analysis reported by Moravec. Originality/value – The hybrid method proposed in this paper, combining RP and FKM, is new. Experimental results indicate that the proposed method is useful for reducing dimensionality of high‐dimensional data such as images, text, etc.

Journal

International Journal of Intelligent Computing and CyberneticsEmerald Publishing

Published: Aug 23, 2011

Keywords: Dimensionality reduction; Fuzzy k‐means clustering; Information retrieval; Random projections; Singular value decomposition; Data analysis; Data processing

References

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