Abstract
In this study we propose a discrete-time Hopfield Neural Network based clustering algorithm for text clustering for cases L = 2q where L is the number of clusters and q is a positive integer. The optimum general solution for even 2-cluster case is not known. The main contribution of this paper is as follows: We show that i) sum of intra-cluster distances which is to be minimized by a text clustering algorithm is equal to the Lyapunov (energy) function of the Hopfield Network whose weight matrix is equal to the Laplacian matrix obtained from the document-by-document distance matrix for 2-cluster case; and ii) the Hopfield Network can be iteratively applied to text clustering for L = 2k. Results of our experiments on several benchmark text datasets show the effectiveness of the proposed algorithm as compared to the k-means.
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Uykan, Z., Ganiz, M.C., Şahinli, Ç. (2012). Discrete-Time Hopfield Neural Network Based Text Clustering Algorithm. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34475-6_66
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DOI: https://doi.org/10.1007/978-3-642-34475-6_66
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