Abstract
Feature clustering has evolved to be a powerful method for clustering text documents. In this paper we propose a hybrid similarity based clustering algorithm for feature clustering. Documents are represented by keywords. These words are grouped into clusters, based on efficient similarity computations. Documents with related words are grouped into clusters. The clusters are characterised by similarity equations, graph based similarity measures and Gaussian parameters. As words are been given into the system, clusters would be generated automatically. The hybrid mechanism works with membership algorithms to identify documents that match with one another and can be grouped into clusters. The method works to find the real distribution of words in the text documents. Experimental results do show that the proposed method is much better when compared against several other clustering methods. The distinguished clusters are identified by a unique group of top keywords, obtained from the documents of a cluster.
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Dev, D.D., Jebaruby, M. (2014). Text Clustering Using Novel Hybrid Algorithm. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8397. Springer, Cham. https://doi.org/10.1007/978-3-319-05476-6_2
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DOI: https://doi.org/10.1007/978-3-319-05476-6_2
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