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.
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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
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DOI: https://doi.org/10.1007/978-3-642-20282-7_27
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