Discrete-Time Hopfield Neural Network Based Text Clustering Algorithm

Uykan Z., Ganiz M. C., Sahinli C.

19th International Conference on Neural Information Processing (ICONIP), Doha, Qatar, 11 - 15 Kasım 2012, cilt.7663, ss.551-559 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 7663
  • Doi Numarası: 10.1007/978-3-642-34475-6_66
  • Basıldığı Şehir: Doha
  • Basıldığı Ülke: Qatar
  • Sayfa Sayıları: ss.551-559
  • Marmara Üniversitesi Adresli: Hayır

Özet

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.