Abstract
In this paper, we proposed a new hybrid clustering algorithm based on Vector Quantization (VQ) and Growing-Cell Structure (GCS). The basic idea is using VQ to refine the GCS clustering results and thus to improve the clustering performance. Moreover, the output of the proposed clustering algorithm has a graph structure which is generated gradually during the incremental self-learning process. We evaluate the proposed method on real collections of text documents and the experimental results show that our method achieves better performance comparing with others.
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
Agrawal, R., Imielinski, T. and Swami, A., Mining associations between sets of items in massive databases. In Buneman, P., Jajodia, S. (eds). Proceedings of the ACM SIGMOD international conference on management of data, Washington DC, May 1993, pp 207–216
Fritzke, B., Growing cell structures-a self-organizing network for unsupervised and supervised learning. Neural Networks, 7(9):-1460, 1994.
Kohonen, T., Self-organized formation of topologically correct feature maps. Biological Cybernetics, pp 43–69, 1982
Kohonen, T., Learning vector quantization. In M. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 537–540. MIT Press, 1995.
Van Rijsbergen, C. J., Information Retrieval. 2nd edition, London, Butterworths, 1979.
Rocchio, J., Document Retrieval Systems — Optimization and Evaluation. PhD. Thesis, Harvard University, 1966.
Salton, G. and Buckley, C. Term-weighting approaches in automatic text retrieval. Information Processing and Management, 24:513–523. 1988
Shannon, C., E., A Mathematical Theory of Communication, Bell Syst. Tech. J., 27, 379–423, 623–656. 1948
Voorhees, E. M., Implementing agglomerative hierarchical clustering algorithms for use in document retrieval. Information Processing and Management, 22:465–476, 1986
http://www.daviddlewis.com/resources/testcollections/reuters21578/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Su, Z., Zhang, L., Pan, Y. (2003). Document Clustering Based on Vector Quantization and Growing-Cell Structure. In: Chung, P.W.H., Hinde, C., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2003. Lecture Notes in Computer Science(), vol 2718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45034-3_33
Download citation
DOI: https://doi.org/10.1007/3-540-45034-3_33
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40455-2
Online ISBN: 978-3-540-45034-4
eBook Packages: Springer Book Archive