Data Mining: The Textbook
Yüklə 17,13 Mb.
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Social Network Data Analytics, Springer, pp. 177–214, 2011.
Y. Sun, J. Han, C. Aggarwal, and N. Chawla. When will it happen?: relationship prediction in heterogeneous information networks. ACM international conference on Web search and data mining, pp. 663–672, 2012. P.-N Tan, M. Steinbach, and V. Kumar. Introduction to data mining. Addison-Wesley, 2005. P. N. Tan, V. Kumar, and J. Srivastava. Selecting the right interestingness measure for association patterns. ACM KDD Conference, pp. 32–41, 2002. J. Tang, Z. Chen, A. W.-C. Fu, and D. W. Cheung. Enhancing effectiveness of outlier detection for low density patterns. PAKDD Conference, pp. 535–548, 2002. J. Tang, J. Sun, C. Wang, and Z. Yang. Social influence analysis in large-scale net-works. ACM SIGKDD international conference on Knowledge discovery and data min-ing, pp. 807–816, 2009. B. Taskar, M. Wong, P. Abbeel, and D. Koller. Link prediction in relational data. Advances in Neural Information Processing Systems, 2003. J. Tenenbaum, V. De Silva, and J. Langford. A global geometric framework for non-linear dimensionality reduction. Science, 290 (5500), pp. 2319–2323, 2000. K. Ting, and I. Witten. Issues in stacked generalization. Journal of Artificial Intelli-gence Research, 10, pp. 271–289, 1999. T. Mitsa. Temporal data mining. CRC Press, 2010. H. Toivonen. Sampling large databases for association rules. VLDB Conference, 134–145, 1996. V. Vapnik. The nature of statistical learning theory. Springer, 2000. J. Vaidya. A survey of privacy-preserving methods across vertically partitioned data. Privacy-Preserving Data Mining: Models and Algorithms, Springer, pp. 337–358, 2008. V. Vapnik. Statistical learning theory. Wiley, 1998. V. Verykios, and A. Gkoulalas-Divanis. A Survey of Association Rule Hiding Meth-ods for Privacy. Privacy-Preserving Data Mining: Models and Algorithms, Springer, 267–289, 2008. J. S. Vitter. Random sampling with a reservoir. ACM Transactions on Mathematical Software (TOMS), 11(1), pp. 37–57, 2006. M. Vlachos, M. Hadjieleftheriou, D. Gunopulos, and E. Keogh. Indexing multi-dimensional time-series with support for multiple distance measures. ACM KDD Con-ference, pp. 216–225, 2003.
M. Vlachos, G. Kollios, and D. Gunopulos. Discovering similar multidimensional tra-jectories. IEEE International Conference on Data Engineering, pp. 673–684, 2002. T. De Vries, S. Chawla, and M. Houle. Finding local anomalies in very high dimen-sional space. IEEE ICDM Conference, pp. 128–137, 2010. A. Waddell, and R. Oldford. Interactive visual clustering of high dimensional data by exploring low-dimensional subspaces. INFOVIS, 2012. H. Wang, W. Fan, P. Yu, and J. Han. Mining concept-drifting data streams using ensemble classifiers. ACM KDD Conference, pp. 226–235, 2003. J. Wang, J. Han, and J. Pei. Closet+: Searching for the best strategies for mining frequent closed itemsets. ACM KDD Conference, pp. 236–245, 2003. J. Wang, Y. Zhang, L. Zhou, G. Karypis, and C. C. Aggarwal. Discriminating subse-quence discovery for sequence clustering. SIAM Conference on Data Mining, pp. 605– 610, 2007. W. Wang, J. Yang, and R. Muntz. STING: A statistical information grid approach to spatial data mining. VLDB Conference, pp. 186–195, 1997. J. S. Walker. Fast fourier transforms. CRC Press, 1996. S. Wasserman. Social network analysis: Methods and applications. Cambridge Uni-versity Press, 1994. D. Watts, and D. Strogatz. Collective dynamics of ‘small-world’ networks. Nature, 393 (6684), pp. 440–442, 1998. L. Wei, E. Keogh, and X. Xi. SAXually Explicit images: Finding unusual shapes. Yüklə 17,13 Mb. Dostları ilə paylaş:
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