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SOM-Based Clustering of Textual Documents Using WordNet
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Author(s): Abdelmalek Amine (Djillali Liabes University, Algeria & Taher Moulay University Center, Algeria), Zakaria Elberrichi (Djillali Liabes University, Algeria), Michel Simonet (Joseph Fourier University, France), Ladjel Bellatreche (University of Poitiers, France)and Mimoun Malki (Djillali Liabes University, Algeria)
Copyright: 2009
Pages: 12
Source title:
Handbook of Research on Text and Web Mining Technologies
Source Author(s)/Editor(s): Min Song (New Jersey Institute of Technology, USA)and Yi-Fang Brook Wu (New Jersey Institute of Technology, USA)
DOI: 10.4018/978-1-59904-990-8.ch012
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Abstract
The classification of textual documents has been the subject of many studies. Technologies like the Web and numerical libraries facilitated the exponential growth of available documentation. The classification of textual documents is very important since it allows the users to effectively and quickly fly over and understand better the contents of large corpora. Most classification approaches use the supervised method of training, more suitable with small corpora and when human experts are available to generate the best classes of data for the training phase, which is not always feasible. The unsupervised classification or “clustering” methods make emerge latent (hidden) classes automatically with minimum human intervention, There are many, and the SOM (self Organized Maps) by Kohonen is one of the algorithms for unsupervised classification that gather a certain number of similar objects in groups without a priori knowledge. This chapter introduces the concept of unsupervised classification of textual documents and proposes an experiment with a conceptual approach for the representation of texts and the method of Kohonen for clustering.
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