Abstract
Document clustering based on semantics is a fundamental method of helping users to search and browse in large cllections of documents. Recently a number of papers have reported the applications of self-organizing artificial neural networks in document clustering based on semantics. In particular Growing Neural Gas is a growing neural network that allows the user to reproduce the topological distribution of the inputs, but the structure obtained often has the same complexity as the input data structure; if the input space has more than three dimensions it is impossible to visualize or represent the GNG network as well as the input data structure. In this paper the authors propose a LBG modified network, called LBG-m, that can simplify the GNG structure in order to visualize and summarize it. The two algorithms constitute a tool for browsing large document sets and generating a set of semantic links between clusters of similar documents.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fritzke B., “Growing Self-Organizing Networks-Why?”, ESANN’96: European Symposium on Artificial Neural Networks, Brussels 1995, p. 61–72
Linde Y., Buzo A., Gray R. M., “An Algorithm for Vector Quantizer Design”, IEEE Transactions on Communication, COM-28:84–95, 1980.
Kohonen T., “Self Organizing Maps”, Springer Verlag
Tesauro G., Touretzky D. S., Leen T. K. (eds.) “A growing Neural Gas Network Learns Topologies”, Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995, p. 625–632.
Fritzke B., “The LBG-U method for vector quantization-an improvement over LBG inspired from neural networks”. Neural Processing Letters, 5(1), 1997.
Fritzke B., “A growing neural gas network learns topologies”, NIPS 1994, Denver.
Balabanovic M., Shoham Y., “Learning Information Retrieval Agents: Experiments with Automated Web Browsing”, Proceedings of the AAAI Spring Symposium on Information Gathering from Heterogenous, Distributed Resources, Stanford, CA, March 1995.
Rizzo R. “Self Organizing Networks to Map Information Space in Hypertext Development”, Proceedings of the International ICSC/IFAC Symposium on Neural Computation NC’98, September 23–25, 1998, Vienna, Austria.
Rizzo R., Allegra M., Fulantelli G., Hypertext-like Structures through a SOM Network, in Proc. of ACM Hypertext’ 99, (Darmstadt, Germany, Feb. 21–25, 1999).
Rizzo R., Allegra M., Fulantelli G., Hy.Doc: a System to Support the Study of Large Document Collections, in Proc. of ICL99 workshop, (Villach, Austria, Oct. 7–8, 1999). ISBN 3-7068-0755-6.
Salton G., Allan j., Buckel C, Automatic Structuring and Retrieval of Large Text Files, Communications of ACM, 37,2, 1994, pp. 97–108
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rizzo, R., Munna, E.G. (2000). A Neural Network Tool to Organize Large Document Sets. In: Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2000. Lecture Notes in Computer Science, vol 1904. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45331-8_29
Download citation
DOI: https://doi.org/10.1007/3-540-45331-8_29
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41044-7
Online ISBN: 978-3-540-45331-4
eBook Packages: Springer Book Archive