Data Mining: The Textbook
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W. Chen, C. Wang, and Y. Wang. Scalable influence maximization for prevalent viral marketing in large-scale social networks. ACM KDD Conference, pp. 1029–1038, 2010. W. Chen, Y. Yuan, and L. Zhang. Scalable influence maximization in social networks under the linear threshold model. IEEE International Conference on Data Mining, 88–97, 2010. D. Chen, C.-T. Lu, Y. Chen, and D. Kou. On detecting spatial outliers. Geoinformat-ica, 12: pp. 455–475, 2008. T. Cheng, and Z. Li. A hybrid approach to detect spatialtemporal outliers. Interna-tional Conference on Geoinformatics, pp. 173–178, 2004. T. Cheng, and Z. Li. A multiscale approach for spatio-temporal outlier detection. Transactions in GIS, 10(2), pp. 253–263, March 2006. Y. Cheng. Mean shift, mode seeking, and clustering. IEEE Transactions on PAMI, 17(8), pp. 790–799, 1995. H. Cheng, X. Yan, J. Han, and C. Hsu. Discriminative frequent pattern analysis for effective classification. ICDE Conference, pp. 716–725, 2007. F. Y. Chin, and G. Ozsoyoglu. Auditing and inference control in statistical databases. IEEE Transactions on Software Enginerring, 8(6), pp. 113–139, April 1982. B. Chiu, E. Keogh, and S. Lonardi. Probabilistic discovery of time series motifs. ACM KDD Conference, pp. 493–498, 2003. F. Chung. Spectral Graph Theory. Number 92 in CBMS Conference Series in Math-ematics, American Mathematical Society, 1997. V. Ciriani, S. De Capitani di Vimercati, S. Foresti, and P. Samarati. k-anonymous data mining: A survey. Privacy-preserving data mining: models and algorithms, Springer, 105–136, 2008. C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, and M. Y. Zhu. Tools for privacy preserving distributed data mining. ACM SIGKDD Explorations Newsletter, 4(2), 28–34, 2002. N. Cristianini, and J. Shawe-Taylor. An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, 2000. W. Cochran. Sampling techniques. John Wiley and Sons, 2007. D. Cohn, L. Atlas, and R. Ladner. Improving generalization with active learning. Machine Learning, 5(2), pp. 201–221, 1994. D. Cohn, Z. Ghahramani, and M. Jordan. Active learning with statistical models. Journal of Artificial Intelligence Research, 4, pp. 129–145, 1996. D. Comaniciu, and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on PAMI, 24(5), pp. 603–619, 2002. D. Cook, and L. Holder. Graph-based data mining. IEEE Intelligent Systems, 15(2), 32–41, 2000. 704 BIBLIOGRAPHY R. Cooley, B. Mobasher, and J. Srivastava. Data preparation for mining world wide web browsing patterns. Knowledge and information systems, 1(1), pp. 5–32, 1999. L. P. Cordella, P. Foggia, C. Sansone, and M. Vento. A (sub)graph isomorphism algo-rithm for matching large graphs. IEEE Transactions on Pattern Mining and Machine Intelligence, 26(10), pp. 1367–1372, 2004. H. Shang, Y. Zhang, X. Lin, and J. X. Yu. Taming verification hardness: an efficient algorithm for testing subgraph isomorphism. Proceedings of the VLDB Endowment, 1(1), pp. 364–375, 2008. J. R. Ullmann. An algorithm for subgraph isomorphism. Journal of the ACM, 23: 31–42, January 1976. G. Cormode, and S. Muthukrishnan. An improved data stream summary: the count-min sketch and its applications. Journal of Algorithms, 55(1), pp. 58–75, 2005. S. Cost, and S. Salzberg. A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning, 10(1), pp. 57–78, 1993. T. Cover, and P. Hart. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), pp. 21–27, 1967. D. Cutting, D. Karger, J. Pedersen, and J. Tukey. Scatter/gather: A cluster-based approach to browsing large document collections. ACM SIGIR Conference, pp. 318– 329, 1992. M. Dash, K. Choi, P. Scheuermann, and H. Liu. Feature selection for clustering-a filter solution. ICDM Conference, pp. 115–122, 2002. M. Deshpande, and G. Karypis. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS), 22(1), pp. 143–177, 2004. I. Dhillon. Co-clustering documents and words using bipartite spectral graph parti-tioning, ACM KDD Conference, pp. 269–274, 2001. I. Dhillon, S. Mallela, and D. Modha. Information-theoretic co-clustering. ACM KDD Conference, pp. 89–98, 2003. I. Dhillon, Y. Guan, and B. Kulis. Kernel k-means: spectral clustering and normalized cuts. ACM KDD Conference, pp. 551–556, 2004. P. Domingos. MetaCost: A general framework for making classifiers cost-sensitive. ACM KDD Conference, pp. 155–164, 1999. P. Domingos. Bayesian averaging of classifiers and the overfitting problem. ICML Conference, pp. 223–230, 2000. P. Domingos, and G. Hulten. Mining high-speed data streams. ACM KDD Conference, 71–80. 2000. P. Clark, and T. Niblett. The CN2 induction algorithm. Machine Learning, 3(4), 261–283, 1989. W. W. Cohen. Fast effectve rule induction. ICML Conference, pp. 115–123, 1995.
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