Data Mining: The Textbook
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S. A. Macskassy, and F. Provost. Classification in networked data: A toolkit and a univariate case study. Joirnal of Machine Learning Research, 8, pp. 935–983, 2007. G. Qi, C. Aggarwal, and T. Huang. Link Prediction across networks by biased cross-network sampling. IEEE ICDE Conference, pp. 793–804, 2013. G. Qi, C. Aggarwak, and T. Huang. Online community detection in social sensing. ACM WSDM Conference, pp. 617–626, 2013. J. Quinlan. C4.5: programs for machine learning. Morgan-Kaufmann Publishers, 1993. J. Quinlan. Induction of decision trees. Machine Learning, 1, pp. 81–106, 1986. D. Rafiei, and A. Mendelzon. Similarity-based queries for time series data, ACM SIGMOD Record, 26(2), pp. 13–25, 1997. E. Rahm, and H. Do. Data cleaning: problems and current approaches, IEEE Data Engineering Bulletin, 23(4), pp. 3–13, 2000. R. Ramakrishnan, and J. Gehrke. Database Management Systems. Osborne/McGraw Hill, 1990. V. Raman, and J. Hellerstein. Potter’s wheel: An interactive data cleaning system. VLDB Conference, pp. 381–390, 2001. S. Ramaswamy, R. Rastogi, and K. Shim. Efficient algorithms for mining outliers from large data sets. ACM SIGMOD Conference, pp. 427–438, 2000. M. Rege, M. Dong, and F. Fotouhi. Co-clustering documents and words using bipartite isoperimetric graph partitioning. IEEE ICDM Conference, pp. 532–541, 2006. E. S. Ristad, and P. N. Yianilos. Learning string-edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(5), pp. 522–532, 1998. F. Rosenblatt. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 286, 1958. R. Salakhutdinov, and A. Mnih. Probabilistic Matrix Factorization. Advances in Neu-ral and Information Processing Systems, pp. 1257–1264, 2007. G. Salton, and M. J. McGill. Introduction to modern information retrieval. McGraw Hill, 1986. P. Samarati. Protecting respondents identities in microdata release. IEEE Transac-tions on Knowledge and Data Engineering, 13(6), pp. 1010–1027, 2001. H. Samet. The design and analysis of spatial data structures. Addison-Wesley, Read-ing, MA, 1990. J. Sander, M. Ester, H. P. Kriegel, and X. Xu. Density-based clustering in spatial databases: The algorithm gdbscan and its applications. Data Mining and Knowledge Discovery, 2(2), pp. 169–194, 1998. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. World Wide Web Conference, pp. 285–295, 2001. 720 BIBLIOGRAPHY A. Savasere, E. Omiecinski, and S. B. Navathe. An efficient algorithm for mining association rules in large databases. Very Large Databases Conference, pp. 432–444, 1995. A. Savasere, E. Omiecinski, and S. Navathe. Mining for strong negative associations in a large database of customer transactions. IEEE ICDE Conference, pp. 494–502, 1998. C. Saunders, A. Gammerman, and V. Vovk. Ridge regression learning algorithm in dual variables. ICML Conference, pp. 515–521, 1998. B. Scholkopf, and A. J. Smola. Learning with kernels: support vector machines, reg-ularization, optimization, and beyond. Cambridge University Press, 2001. B. Scholkopf, A. Smola, and K.-R. Muller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), pp. 1299–1319, 1998. B. Scholkopf, and A. J. Smola. Learning with Kernels. MIT Press, Cambridge, MA, 2002. H. Schutze, and C. Silverstein. Projections for efficient document clustering. ACM SIGIR Conference, pp. 74–81, 1997. F. Sebastiani. Machine Learning in Automated Text Categorization. ACM Computing Surveys, 34(1), 2002. B. Settles. Active Learning. Morgan and Claypool, 2012. B. Settles, and M. Craven. An analysis of active learning strategies for sequence label-ing tasks. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1069–1078, 2008. D. Seung, and L. Lee. Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, pp. 556–562, 2001. H. Seung, M. Opper, and H. Sompolinsky. Query by committee. Fifth annual workshop on Computational learning theory, pp. 287–294, 1992. J. Shafer, R. Agrawal, and M. Mehta. SPRINT: A scalable parallel classifier for data mining. VLDB Conference, pp. 544–555, 1996. S. Shekhar, C. T. Lu, and P. Zhang. Detecting graph-based spatial outliers: algorithms and applications. ACM KDD Conference, pp. 371–376, 2001. S.Shekhar, C. T. Lu, and P. Zhang. A unified approach to detecting spatial outliers. Geoinformatica, 7(2), pp. 139–166, 2003. S. Shekhar, and S. Chawla. A tour of spatial databases. Prentice Hall, 2002. S. Shekhar, C. T. Lu, and P. Zhang. Detecting graph-based spatial outliers. Intelligent Data Analysis, 6, pp. 451–468, 2002. S. Shekhar, and Y. Huang. Discovering spatial co-location patterns: a summary of results. In Advances in Spatial and Temporal Databases , pp. 236–256, Springer, 2001.
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