Abstract
This paper focuses on the use of self-organising maps, also known as Kohonen maps, for the classification task of text documents. The aim is to effectively and automatically classify documents to separate classes based on their topics. The classification with self-organising map was tested with three data sets and the results were then compared to those of six well known baseline methods: k-means clustering, Ward’s clustering, k nearest neighbour searching, discriminant analysis, Naïve Bayes classifier and classification tree. The self-organising map proved to be yielding the highest accuracies of tested unsupervised methods in classification of the Reuters news collection and the Spanish CLEF 2003 news collection, and comparable accuracies against some of the supervised methods in all three data sets.
This is a preview of subscription content, log in via an institution to check access.
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
Apte, C., Damerau, F.J., Weiss, S.M.: Automated learning of decision rules for text categorization. ACM Transactions on Information Systems 12, 233–251 (1994)
ChandraShekar, B.H., Shobha, G.: Classification of Documents Using Kohonen’s Self-Organizing Map. International Journal of Computer Theory and Engineering 5(1), 610–613 (2009)
Chen, Y., Qin, B., Liu, T., Liu, Y., Li, S.: The Comparison of SOM and K-means for Text Clustering. Computer and Information Science 2(3), 268–274 (2010)
Chowdhury, N., Saha, D.: Unsupervised text classification using kohonen’s self organizing network. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 715–718. Springer, Heidelberg (2005)
Chumwatana, T., Wong, K., Xie, H.: A SOM-Based Document Clustering Using Frequent Max Substring for Non-Segmented Texts. Journal of Intelligent Learning Systems & Applications 2, 117–125 (2010)
CLEF: The Cross-Language Evaluation Forum, http://www.clef-campaign.org/
Conover, W.J.: Practical Nonparametric Statistics. John Wiley & Sons, New York (1999)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, New York (2001)
Eyassu, S., Gambäck, B.: Classifying Amharic News Text Using Self-Organizing Maps. Proceeding of the ACL Workshop on Computational Approaches to Semitic Languages, Ann Arbor, Michigan, USA, pp. 71–78 (2005)
Fernandez, J., Mones, R., Diaz, I., Ranilla, J., Combarro, E.: Experiments with Self Organizing Maps in CLEF 2003. In: Peters, C., Gonzalo, J., Braschler, M., Kluck, M. (eds.) CLEF 2003. LNCS, vol. 3237, pp. 358–366. Springer, Heidelberg (2004)
Guerro-Bote, V.P., Moya-Anegón, F., Herrero-Solana, V.: Document organization using Kohonen’s algorithm. Information Processing and Management 38, 79–89 (2002)
Honkela, T.: Self-Organizing Maps in Natural Language Processing, Academic Dissertation. Helsinki University of Technology, Finland (1997)
Kohonen, T.: Self-Organizing Maps. Springer, Berlin (1995)
Lagus, K.: Text retrieval using self-organized document maps. Neural Processing Letters 15, 21–29 (2002)
Lagus, K., Kaski, S., Kohonen, T.: Mining massive document collections by the WEBSOM method. Information Sciences 163(1-3), 135–156 (2004)
Moya-Anegón, F., Herrero-Solana, V., Jiménez-Contreras, E.: A connectionist and multivariate approach to science maps: the SOM, clustering and MDS applied to library and information science research. Journal of Information Science 32(1), 63–77 (2006)
Reuters-21578 collection, http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html
Saarikoski, J., Laurikkala, J., Järvelin, K., Juhola, M.: A study of the use of self-organising maps in information retrieval. Journal of Documentation 65(2), 304–322 (2009)
Saarikoski, J., Järvelin, K., Laurikkala, J., Juhola, M.: On Document Classification with Self-Organising Maps. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds.) ICANNGA 2009. LNCS, vol. 5495, pp. 140–149. Springer, Heidelberg (2009)
Salton, G.: Automatic Text Processing: The Transformation, Analysis and Retrieval of Information by Computer. Addison-Wesley, Reading (1989)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)
SOM_PAK, http://www.cis.hut.fi/research/som-research/nnrc-programs.shtml
20 newsgroups collection, http://people.csail.mit.edu/jrennie/20Newsgroups/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Saarikoski, J., Laurikkala, J., Järvelin, K., Juhola, M. (2011). Self-Organising Maps in Document Classification: A Comparison with Six Machine Learning Methods. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20282-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-20282-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20281-0
Online ISBN: 978-3-642-20282-7
eBook Packages: Computer ScienceComputer Science (R0)