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.
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