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H-BayesClust: A new hierarchical clustering based on Bayesian networks

Haghir Chehreghani, M ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-540-73871-8_59
  3. Publisher: Springer Verlag , 2007
  4. Abstract:
  5. Clustering is one of the most important approaches for mining and extracting knowledge from the web. In this paper a method for clustering the web data is presented which using a Bayesian network, finds appropriate representatives for each of the clusters. Having those representatives, we can create more accurate clusters. Also the contents of the web pages are converted into vectors which firstly, the number of dimensions is reduced, and secondly the orthogonality problem is solved. Experimental results show about the high quality of the resultant clusters. © Springer-Verlag Berlin Heidelberg 2007
  6. Keywords:
  7. Clustering algorithms ; Knowledge acquisition ; Problem solving ; Vectors ; Virtual storage ; World Wide Web ; Social networking (online) ; Cluster center ; Hierarchy ; Orthogonality ; Web clustering ; Belief ; Cluster centers ; Hier-archical clustering ; High quality ; Web data ; Bayesian networks
  8. Source: 3rd International Conference on Advanced Data Mining and Applications, ADMA 2007, Harbin, 6 August 2007 through 8 August 2007 ; Volume 4632 LNAI , 2007 , Pages 616-624 ; 03029743 (ISSN); 9783540738701 (ISBN)
  9. URL: https://link.springer.com/chapter/10.1007%2F978-3-540-73871-8_59