Abstract
We present a method of grouping documents with genetic algorithms, the groups are created from the tokens representing the document. The system select the tokens starting from the Goffman point, selecting an area of suitable transition making use for it of the Zipf law. The experiments are carried out with the collection Reuters 21578 and the genetic algorithm uses the new operators designed to find the affinity and similarity of the documents without having prior knowledge of other characteristics. The proposed method is an alternative to the methods of traditional clustering and the results show that genetic algorithm is robust, clustering the documents in the collection of documents efficiently.
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
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison Wesley, Reading (1999)
Coello, C.A.: Evolutionary Algorithms for solving multi-objective problems. Kluwer, Dordrecht (2002)
David, O., Delen, D.: Advanced Data Mining Techniques. Springer, Heidelberg (2008)
Pao, M.L.: Indexing based on Goffman transition of word occurrences. American Society (1980)
Leung, W.: Data Mining using grammar based genetic programming. Koza (ed.) (2002)
Zipf, G.K.: Human Behavior and the Principle of Least Effort. Addison Wesley, Reading (1949)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Castillo S., J.L., del Castillo, J.R.F., Sotos, L.G. (2009). Group Method of Documentary Collections Using Genetic Algorithms. In: Omatu, S., et al. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol 5518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02481-8_151
Download citation
DOI: https://doi.org/10.1007/978-3-642-02481-8_151
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02480-1
Online ISBN: 978-3-642-02481-8
eBook Packages: Computer ScienceComputer Science (R0)