Data Mining: The Textbook
L. H. Cox. Suppression methodology and statistical disclosure control. Journal of the American Statistical Association, 75(370), pp. 377–385, 1980. E. Cohen, M. Datar, S. Fujiwara, A. Gionis, P. Indyk, R. Motwani, and C. Yang. Find-ing interesting associations without support pruning. IEEE Transactions on Knowl-edge and Data Engineering, 13(1), pp. 64–78, 2001. T. Dalenius, and S. Reiss. Data-swapping: A technique for disclosure control. Journal of statistical planning and inference, 6(1), pp. 73–85, 1982. G. Das, and H. Mannila. Context-based similarity measures for categorical databases. PKDD Conference, pp. 201–210, 2000. B. V. Dasarathy. Nearest neighbor (NN) norms: NN pattern classification techniques. IEEE Computer Society Press, 1990, S. Deerwester, S. Dumais, T. Landauer, G. Furnas, and R. Harshman. Indexing by latent semantic analysis. JASIS, 41(6), pp. 391–407, 1990. C. Ding, X. He, and H. Simon. On the equivalence of nonnegative matrix factorization and spectral clustering. SDM Conference, pp. 606–610, 2005. J. Domingo-Ferrer, and J. M. Mateo-Sanz. Practical data-oriented microaggregation for statistical disclosure control. IEEE Transactions on Knowledge and Data Engi-neering, 14(1), pp. 189–201, 2002. P. Domingos, and M. Pazzani. On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning, 29(2–3), pp. 103–130, 1997. W. Du, and M. Atallah. Secure multi-party computation: A review and open problems. CERIAS Tech. Report, 2001-51, Purdue University, 2001. R. Duda, P. Hart, and D. Stork. Pattern classification. John Wiley and Sons, 2012. C. Dwork. Differential privacy: A survey of results. Theory and Applications of Models of Computation, Springer, pp. 1–19, 2008. C. Dwork. A firm foundation for private data analysis. Communications of the ACM, 54(1), pp. 86–95, 2011. D. Easley, and J. Kleinberg. Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press, 2010. C. Elkan. The foundations of cost-sensitive learning. IJCAI, pp. 973–978, 2001. R. Elmasri, and S. Navathe. Fundamentals of Database Systems. Addison-Wesley, 2010. L. Ertoz, M. Steinbach, and V. Kumar. A new shared nearest neighbor clustering algorithm and its applications. Workshop on Clustering High Dimensional Data and its Applications, pp. 105–115, 2002. P. Erdos, and A. Renyi. On random graphs. Publicationes Mathematicae Debrecen, 6, pp. 290–297, 1959. 706 BIBLIOGRAPHY M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discover-ing clusters in large spatial databases with noise. ACM KDD Conference, pp. 226–231, 1996. M. Ester, H. P. Kriegel, J. Sander, M. Wimmer, and X. Xu. Incremental clustering for mining in a data warehousing environment. VLDB Conference, pp. 323–333, 1998. S. Even, O. Goldreich, and A. Lempel. A randomized protocol for signing contracts. Communications of the ACM, 28(6), pp. 637–647, 1985. A. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke. Privacy preserving mining of association rules. Information Systems, 29(4), pp. 343–364, 2004. M. Faloutsos, P. Faloutsos, and C. Faloutsos. On power-law relationships of the inter-net topology. ACM SIGCOMM Computer Communication Review, pp. 251–262, 1999. C. Faloutsos, and K. I. Lin. Fastmap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. ACM SIGMOD Conference, pp. 163–174, 1995. W. Fan, S. Stolfo, J. Zhang, and P. Chan. AdaCost: Misclassification cost sensitive boosting. ICML Conference, pp. 97–105, 1999. T. Fawcett. ROC Graphs: Notes and Practical Considerations for Researchers. Tech-nical Report HPL-2003-4, Palo Alto, CA, HP Laboratories, 2003. X. Fern, and C. Brodley. Random projection for high dimensional data clustering: A cluster ensemble approach. ICML Conference, pp. 186–193, 2003. C. Fiduccia, and R. Mattheyses. A linear-time heuristic for improving network parti-tions. In IEEE Conference on Design Automation, pp. 175–181, 1982. R. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugen-ics, 7: pp. 179–188, 1936. P. Flajolet, and G. N. Martin. Probabilistic counting algorithms for data base appli-cations. Journal of Computer and System Sciences, 31(2), pp. 182–209, 1985. G. W. Flake. Square unit augmented, radially extended, multilayer perceptrons. Neu-ral Networks: Tricks of the Trade, pp. 145–163, 1998. F. Fouss, A. Pirotte, J. Renders, and M. Saerens. Random-walk computation of sim-ilarities between nodes of a graph with application to collaborative recommendation. Yüklə 17,13 Mb. Dostları ilə paylaş: |