Data Mining: The Textbook
Yüklə 17,13 Mb.
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Bell System Technical Journal, 1970.
A. Khan, N. Li, X. Yan, Z. Guan, S. Chakraborty, and S. Tao. Neighborhood-based fast graph search in large networks. ACM SIGMOD Conference, pp. 901–912, 2011. A. Khan, Y. Wu, C. Aggarwal, and X. Yan. Nema: Fast graph matching with label similarity. Proceedings of the VLDB Endowment, 6(3), pp. 181–192, 2013. D. Kifer, and J. Gehrke. Injecting utility into anonymized datasets. ACM SIGMOD Conference, pp. 217–228, 2006. L. Kissner, and D. Song. Privacy-preserving set operations. Advances in Cryptology– CRYPTO, pp. 241–257, 2005. J. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46(5), pp. 604–632, 1999. S. Knerr, L. Personnaz, and G. Dreyfus. Single-layer learning revisited: a stepwise procedure for building and training a neural network. In J. Fogelman, editor, Neuro-computing: Algorithms, Architectures and Applications. Springer-Verlag, 1990. E. Knorr, and R. Ng. Algorithms for mining distance-based outliers in large datasets. VLDB Conference, pp. 392–403, 1998. E. Knorr, and R. Ng. Finding intensional knowledge of distance-based outliers. VLDB Conference, pp. 211–222, 1999. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8), pp. 30–37, 2009. Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. ACM KDD Conference, pp. 426–434, 2008.
Y. Koren. Collaborative filtering with temporal dynamics. Communications of the ACM,, 53(4), pp. 89–97, 2010. D. Kostakos, G. Trajcevski, D. Gunopulos, and C. Aggarwal. Time series data clus-tering. Data Clustering: Algorithms and Applications, CRC Press, 2013. J. Konstan. Introduction to recommender systems: algorithms and evaluation. ACM Transactions on Information Systems, 22(1), pp. 1–4, 2004. Y. Kou, C. T. Lu, and D. Chen. Spatial weighted outlier detection, SIAM Conference on Data Mining, 2006. A. Krogh, M. Brown, I. Mian, K. Sjolander, and D. Haussler. Hidden Markov models in computational biology: Applications to protein modeling. Journal of molecular biology, 235(5), pp. 1501–1531, 1994. J. B. Kruskal. Nonmetric multidimensional scaling: a numerical method. Psychome-trika, 29(2), pp. 115–129, 1964. B. Kulis, S. Basu, I. Dhillon, and R. Mooney. Semi-supervised graph clustering: a kernel approach. Machine Learning, 74(1), pp. 1–22, 2009. S. Kulkarni, G. Lugosi, and S. Venkatesh. Learning pattern classification: a survey. IEEE Transactions on Information Theory, 44(6), pp. 2178–2206, 1998. M. Kuramochi, and G. Karypis. Frequent subgraph discovery. IEEE International Conference on Data Mining, pp. 313–320, 2001. L. V. S. Lakshmanan, R. Ng, J. Han, and A. Pang. Optimization of constrained frequent set queries with 2-variable constraints. ACM SIGMOD Conference, pp. 157– 168, 1999. P. Langley, W. Iba, and K. Thompson. An analysis of Bayesian classifiers. Proceedings of the National Conference on Artificial Intelligence, pp. 223–228, 1992. A. Lazarevic, and V. Kumar. Feature bagging for outlier detection. ACM KDD Con-ference, pp. 157–166, 2005. K. LeFevre, D. J. DeWitt, and R. Ramakrishnan. Incognito: Efficient full-domain k-anonymity. ACM SIGMOD Conference, pp. 49–60, 2005. K. LeFevre, D. J. DeWitt, and R. Ramakrishnan. Mondrian multidimensional k-anonymity. IEEE International Conference on Data Engineering, pp. 25, 2006. J.-G. Lee, J. Han, and X. Li. Trajectory outlier detection: A partition-and-detect framework. ICDE Conference, pp. 140–149, 2008. J.-G. Lee, J. Han, and K.-Y. Whang. Trajectory clustering: a partition-and-group framework. ACM SIGMOD Conference, pp. 593–604, 2007. J.-G. Lee, J. Han, X. Li, and H. Gonzalez. TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering. Proceedings of the VLDB Endowment, 1(1), pp. 1081–1094, 2008. 714 BIBLIOGRAPHY W. Lee, and D. Xiang. Information theoretic measures for anomaly detection. IEEE Symposium on Security and Privacy, pp. 130–143, 2001. J. Leskovec, D. Huttenlocher, and J. Kleinberg. Predicting positive and negative links in online social networks. World Wide Web Conference, pp. 641–650, 2010. J. Leskovec, J. Kleinberg, and C. Faloutsos. Graphs over time: densification laws, shrinking diameters, and possible explanations. ACM KDD Conference, pp. 177–187, 2005. J. Leskovec, A. Rajaraman, and J. Ullman. Mining of massive datasets. Cambridge University Press, 2012. D. Lewis. Naive Bayes at forty: The independence assumption in information retrieval. Yüklə 17,13 Mb. Dostları ilə paylaş:
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