Von luxburg u 2007 a tutorial on spectral clustering

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von Luxburg, U. (2007). A tutorial on spectral clustering, Statist. Comput. 17 , pp. 395–416. von Luxburg, U., Belkin, M. and Bousquet, O. (2008). Consistency of spectral clustering, Ann. Statist. 36 , pp. 555–586. Wang, R., Wang, Y., Zhang, X. and Chen, L. (2007). Detecting community structure in complex networks by optimal rearrangement clustering, in J. G. Carbonell and J. Siekmann (eds), Papers from the PAKDD 2007 International Workshops (Springer, Berlin), pp. 119–130. Wang, Y. J. and Wong, G. Y. (1987). Stochastic block models for directed graphs, J. Amer. Statist. Assoc. 82 , pp. 8–19. Wasserman, S. and Faust, K. (1994). Social Network Analysis: Methods and Appli- cations (Cambridge University Press, Cambridge). Watts, D. J. and Strogatz, S. H. (1998). Collective dynamics of “small-world” networks, Nature 393 , pp. 440–442. White, S. and Smyth, P. (2005). A spectral clustering approach to finding com- munities in graphs, in Proc. SIAM International Conference on Data Min- ing , pp. 274–285. Available at: http://www.cs.ucr.edu/%7Eeamon/HOT% 20SAX%20%zolong-ver.pdf. Accessed 04.09.13. Copyright © 2014. Imperial College Press. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. EBSCO Publishing : eBook Collection (EBSCOhost) - printed on 2/16/2016 3:37 AM via CGC-GROUP OF COLLEGES (GHARUAN) AN: 779681 ; Heard, Nicholas, Adams, Niall M..; Data Analysis for Network Cyber-security Account: ns224671
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Inference for Graphs and Networks 31 Zachary, W. W. (1977). An information flow model for conflict and fission in small groups, J. Anthropolog. Res. 33 , pp. 452–473. Zheng, T., Salganik, M. J. and Gelman, A. (2006). How many people do you know in prison?: Using overdispersion in count data to estimate social structure in networks, J. Amer. Statist. Assoc. 101 , pp. 409–423. Copyright © 2014. Imperial College Press. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. EBSCO Publishing : eBook Collection (EBSCOhost) - printed on 2/16/2016 3:37 AM via CGC-GROUP OF COLLEGES (GHARUAN) AN: 779681 ; Heard, Nicholas, Adams, Niall M..; Data Analysis for Network Cyber-security Account: ns224671
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This page intentionally left blank Copyright © 2014. Imperial College Press. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. EBSCO Publishing : eBook Collection (EBSCOhost) - printed on 2/16/2016 3:37 AM via CGC-GROUP OF COLLEGES (GHARUAN) AN: 779681 ; Heard, Nicholas, Adams, Niall M..; Data Analysis for Network Cyber-security Account: ns224671
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Chapter 2 Rapid Detection of Attacks in Computer Networks by Quickest Changepoint Detection Methods Alexander G. Tartakovsky University of Southern California, Department of Mathematics 3620 S. Vermont Avenue, KAP-108, Los Angeles, CA 90089-2532, USA [email protected] University of Connecticut, Department of Statistics 215 Glenbrook Road, U-4120, Sorrs, CT 06269, USA [email protected] Changepoint problems deal with detecting changes in a process that occur at unknown points in time. The gist of the quickest changepoint problem is to
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