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Vol: 57(71) No: 3 / September 2012 Projective Dimension of Text Documents in Multidimensional Space using PART Neural Network Roman Krakovsky Department of Informatics, Catholic university in Ruzomberok, Faculty of Pedagogy, Hrabovska cesta 1, 034 01 Ruzomberok, Slovakia, phone: (421) 44-4326844, e-mail: roman.krakovsky@ku.sk Igor Mokris Institute of Informatics, Slovak Academy of Sciences, Dubravska cesta 9, 845 07 Bratislava, Slovakia, e-mail: igor.mokris@savba.sk Keywords: PART neural network, clustering, multi-dimensional space, outlier cluster Abstract The paper aim to clustering of text documents by neural networks. Text documents in our proposal model are saved in Vector Space (VS) model, described by VS matrix. Conventional clustering algorithm have problem with clustering in multidimensional data space because of inherent sparsity of data. The presented approach for creation of subspaces of multidimensional spaces uses the Projective Adaptive Resonance Theory (PART) neural network that enables this way of reduction of multidimensional text document space and also the text document clustering. Efficiency of the text document clustering by subspaces of multidimensional space it is influenced by properties of PART. It means that optimal parameters of PART have to be set. Thanks to exact settings of distance and vigilance parameter of PART it is possible to find the clusters, their centers in the projective dimensions of subspaces and creates outlier cluster for noisy datasets. References [1] G. Salton, A. Singhal, J. Allan, “Automatic Text Decomposition and Structuring,” Information Processing and Management, pp.127-138, 1996. [2] G. G. Chowdhury, Introduction to Modern Information Retrieval. Facet Publishing, 2004. [3] C.C. Aggarwal, C. Procoius, J.L. Wolf, P.S. Yu and J.S. 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