Abstract
In this paper, we propose a new method of representing text documents based on clustering of term frequency vectors. Term frequency vectors of each cluster are used to form a symbolic representation (interval valued representation) by the use of mean and standard deviation. In order to cluster the term frequency vectors, we make use of fuzzy C-Means clustering method for interval type data based on adaptive squared Euclidean distance between vectors of intervals. Further, to corroborate the efficacy of the proposed model we conducted extensive experimentation on standard datasets like 20 Newsgroup Large, 20 Mini Newsgroup, Vehicles Wikipedia and our own created datasets like Google Newsgroup and Research Article Abstracts. We have compared our classification accuracy achieved by the Symbolic classifier with the other existing Naïve Bayes classifier, KNN classifier, Centroid based classifier and SVM classifiers. The experimental results reveal that the achieved classification accuracy is better than that of the existing methods. In addition, our method is based on a simple matching scheme; it requires negligible time for classification.
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Harish, B.S., Prasad, B., Udayasri, B. (2014). Classification of Text Documents Using Adaptive Fuzzy C-Means Clustering. In: Thampi, S., Abraham, A., Pal, S., Rodriguez, J. (eds) Recent Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 235. Springer, Cham. https://doi.org/10.1007/978-3-319-01778-5_21
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DOI: https://doi.org/10.1007/978-3-319-01778-5_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01777-8
Online ISBN: 978-3-319-01778-5
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