Discrete-Time Hopfield Neural Network Based Text Clustering Algorithm
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 = 2k. Results of our experiments on several benchmark text datasets show the effectiveness of the proposed algorithm as compared to the k-means.
KeywordsText clustering discrete-time Hopfield Neural Networks Lyapunov function max-cut graph partitioning
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