Abstract
Data clustering is an important technique for exploratory data analysis. The speed, reliability and consistency with which a clustering algorithm can organize large amounts of data constitute reasons to use it in applications like data mining, document retrieval, signal compression, coding and pattern classification. In this paper, we use clustering for efficient large-scale pattern classification; more specifically, we achieve it by selecting appropriate prototypes and features using Singular Value Decomposition (SVD). It is found that the SVD based clustering not only selects better prototypes, but also reduces the memory and computational requirements by 98% over the conventional Nearest Neighbour Classifier (NNC) (T.M.Cover and P.E.Hart (1967)), on OCR data.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
ANIL K. JAIN, ROBERT P.W. DULIN and JIANCHANG MAO. (2000): Statistical Pattern Recognition: A Review. IEEE Trans. Pattern Analysis and Machine Intelligence. Vol. 22, No. 1, pp. 4–37.
COVER, T.M. and HART, P.E. (1967): Nearest Neighbour Pattern Classification. IEEE Trans. Information Theory. vol. 13, no. 1, pp. 21–27.
DEWILDE, P. and DEPRETTERE, ED.F. (1988): Singular Value Decomposition: An introduction. In: Ed. F. Deprettere, editor, SVD and Signal Processing: Algorithms, Applications, and Architectures. Elsevier Science Publishers, North Holland, pp. 3–41.
DRINCAS, P., ALAN FRIEZE, RAVI KANNAN, SANTOSH VEMPALA, VINAY, V. (1999): Clustering in large graphs and matrices. Proc. of the symposium on Discrete Algorithms, SIAM
JAIN, A.K., MURTY, M.N. and FLYNN, P.J. (1999): Data clustering: a review. ACM computing surveys. Vol 31, Issue 3, Nov pp-264–323.
JAIN, A.K. and CHANDRASEKARAN, R. (1982): Dimensionality and sample size considerations in pattern recognition practice, in: Handbook of Dimensionality. P.R. Krishnaiah and L.N. Kanal, Eds. New York: North-Holland
PRAKASH, M. and NARASIMHA MURTY, M. (1997): Growing subspace pattern recognition methods and their neural-network models. IEEE Trans. Neural Networks. Vol. 8, No. 1, pp. 161–168.
RICHARD O. DUDA, PETER E. HART and DAVID G. STORK. (2000): Pattern Classification (2nd ed.)
YOSHIHIKO HAMAMOTO, SHUNJI UCHIMURA and SHINGO TOMITA. (1997): A Bootstrap Technique for Nearest neighbour Classifier. IEEE Trans.Pattern Analysis and Machine Intelligence. Vol 19, no 1, Jan pp. 73–79.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sai Jayram, A.K.V., Murty, M.N. (2002). Clustering for Prototype Selection using Singular Value Decomposition. In: Jajuga, K., Sokołowski, A., Bock, HH. (eds) Classification, Clustering, and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56181-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-56181-8_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43691-1
Online ISBN: 978-3-642-56181-8
eBook Packages: Springer Book Archive