Discrete - time hopfield neural network based text clustering algorithm
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CitationUYKAN, Z., GANİZ, M.C., ŞAHİNLİ, Ç. (2012). Discrete - time hopfield neural network based text clustering algorithm. In HUANG, T., ZENG, Z., Li, C, LEUNG, C.S. (eds), Lecture Notes in Computer Science, Vol. 7663, pp. 551-559. http://dx.doi.org/10.1007/978-3-642-34475-6_66.
In this study we propose a discrete-time Hopfield Neural Network based clustering algorithm for text clustering for cases L = 2(q) 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 = 2(k). Results of our experiments on several benchmark text datasets show the effectiveness of the proposed algorithm as compared to the k-means.