Data Mining: The Textbook



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IEEE ICDM Conference, pp. 711–720, 2006.



  1. H. Wiener. Structural determination of paraffin boiling points. Journal of the Amer-ican Chemical Society. 1(69). pp. 17–20, 1947.




  1. L. Willenborg, and T. De Waal. Elements of statistical disclosure control. Springer, 2001.




  1. D. Wolpert. Stacked generalization. Neural Networks, 5(2), pp. 241–259, 1992.




  1. X. Xiao, and Y. Tao. Anatomy: Simple and effective privacy preservation. Very Large Databases Conference, pp. 139–150, 2006.




  1. D. Xin, J. Han, X. Yan, and H. Cheng. Mining compressed frequent-pattern sets. VLDB Conference, pp. 709–720, 2005.




  1. Z. Xing, J. Pei, and E. Keogh. A brief survey on sequence classification. SIGKDD Explorations Newsletter, 12(1), pp. 40–48, 2010.




  1. H. Xiong, P. N. Tan, and V. Kumar. Mining strong affinity association patterns in data sets with skewed support distribution. ICDM Conference, pp. 387–394, 2003.




  1. K. Yaminshi, J. Takeuchi, and G. Williams. Online unsupervised outlier detec-tion using finite mixtures with discounted learning algorithms, ACM KDD Confer-ence,pp. 320–324, 2000.

724 BIBLIOGRAPHY





  1. X. Yan, and J. Han. gSpan: Graph-based substructure pattern mining. IEEE Inter-national Conference on Data Mining, pp. 721–724, 2002.




  1. X. Yan, P. Yu, and J. Han. Substructure similarity search in graph databases. ACM SIGMOD Conference, pp. 766–777, 2005.




  1. X. Yan, P. Yu, and J. Han. Graph indexing: a frequent structure-based approach.



ACM SIGMOD Conference, pp. 335–346, 2004.



  1. X. Yan, F. Zhu, J. Han, and P. S. Yu. Searching substructures with superimposed distance. International Conference on Data Engineering, pp. 88, 2006.




  1. J. Yang, and W. Wang. CLUSEQ: efficient and effective sequence clustering. IEEE International Conference on Data Engineering, pp. 101–112, 2003.




  1. D. Yankov, E. Keogh, J. Medina, B. Chiu, and V. Zordan. Detecting time series motifs under uniform scaling. ACM KDD Conference, pp. 844–853, 2007.




  1. N. Ye. A markov chain model of temporal behavior for anomaly detection. IEEE Information Assurance Workshop, pp. 169, 2004.




  1. B. K. Yi, H. V. Jagadish, and C. Faloutsos. Efficient retrieval of similar time sequences under time warping. IEEE International Conference on Data Engineering, pp. 201– 208, 1998.




  1. B. K. Yi, N. Sidiropoulos, T. Johnson, H. V. Jagadish, C. Faloutsos, and A. Biliris. Online data mining for co-evolving time sequences. International Conference on Data Engineering, pp. 13–22, 2000.




  1. H. Yildirim, and M. Krishnamoorthy. A random walk method for alleviating the sparsity problem in collaborative filtering. ACM conference on Recommender systems, pp. 131–138, 2008.




  1. X. Yin, and J. Han. CPAR: Classification based on predictive association rules. SIAM international conference on data mining, pp. 331–335, 2003.




  1. S. Yu, and J. Shi. Multiclass spectral clustering. International Conference on Com-puter Vision, 2003.




  1. B. Zadrozny, J. Langford, and N. Abe. Cost-sensitive learning by cost-proportionate example weighting. ICDM Conference, pp. 435–442, 2003.




  1. R. Zafarani, M. A. Abbasi, and H. Liu. Social media mining: an introduction. Cam-bridge University Press, New York, 2014.




  1. H. Zakerzadeh, C. Aggarwal, and K. Barker. Towards breaking the curse of dimension-ality for high-dimensional privacy. SIAM Conference on Data Mining, pp. 731–739, 2014.




  1. M. J. Zaki. Scalable algorithms for association mining. IEEE Transactions on Knowl-edge and Data Engineering, 12(3), pp. 372–390, 2000.




  1. M. J. Zaki. SPADE: An efficient algorithm for mining frequent sequences. Machine learning, 42(1–2), pp. 31–60, 2001. 31–60.


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