Data Mining: The Textbook



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BIBLIOGRAPHY

709




  1. J. Han, G. Dong, and Y. Yin. Efficient mining of partial periodic patterns in time series database. International Conference on Data Engineering, pp. 106–115, 1999.




  1. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation.



ACM SIGMOD Conference, pp. 1–12, 2000.



  1. J. Han, H. Cheng, D. Xin, and X. Yan. Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery, 15(1), pp. 55–86, 2007.




  1. J. Haslett, R. Brandley, P. Craig, A. Unwin, and G. Wills. Dynamic graphics for exploring spatial data with application to locating global and local anomalies. The American Statistician, 45: pp. 234–242, 1991.




  1. T. Hastie, and R. Tibshirani. Discriminant adaptive nearest neighbor classification.



IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(6), pp. 607–616, 1996.



  1. T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning.



Springer, 2009.



  1. V. Hautamaki, V. Karkkainen, and P. Franti. Outlier detection using k-nearest neigh-bor graph. International Conference on Pattern Recognition, pp. 430–433, 2004.




  1. T. H. Haveliwala. Topic-sensitive pagerank. World Wide Web Conference, pp. 517-526, 2002.




  1. D. M. Hawkins. Identification of outliers. Chapman and Hall, 1980.




  1. S. Haykin. Kalman filtering and neural networks. Wiley, 2001.




  1. S. Haykin. Neural networks and learning machines. Prentice Hall, 2008.




  1. X. He, D. Cai, and P. Niyogi. Laplacian score for feature selection. Advances in Neural Information Processing Systems, 18, 507, 2006.




  1. Z. He, X. Xu, J. Huang, and S. Deng. FP-Outlier: Frequent pattern-based outlier detection. COMSIS, 2(1), pp. 103–118, 2005.




  1. Z. He, X. Xu, and S. Deng. Discovering cluster-based local outliers, Pattern Recogni-tion Letters, Vol 24(9–10), pp. 1641–1650, 2003.




  1. M. Henrion, D. Hand, A. Gandy, and D. Mortlock. CASOS: A subspace method for anomaly detection in high-dimensional astronomical databases. Statistical Analysis and Data Mining, 2012.

Online first: http://onlinelibrary.wiley.com/enhanced/doi/10.1002/sam.11167/





  1. A. Hinneburg, C. Aggarwal, and D. Keim. What is the nearest neighbor in high-dimensional space? VLDB Conference, pp. 506–516, 2000.




  1. A. Hinneburg, and D. Keim. An efficient approach to clustering in large multimedia databases with noise. ACM KDD Conference, pp. 58–65, 1998.




  1. A. Hinneburg, D. A. Keim, and M. Wawryniuk. HD-Eye: Visual mining of high-dimensional data. Computer Graphics and Applications, 19(5), pp. 22–31, 1999.

710 BIBLIOGRAPHY





  1. A. Hinneburg, and H. Gabriel. DENCLUE 2.0: Fast clustering based on kernel-density estimation. Intelligent Data Analysis, Springer, pp. 70–80, 2007.




  1. D. S. Hirschberg. Algorithms for the longest common subsequence problem. Journal of the ACM (JACM), 24(4), pp. 664–675, 1975.




  1. T. Hofmann. Probabilistic latent semantic indexing. ACM SIGIR Conference, pp. 50– 57, 1999.




  1. T. Hofmann. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems (TOIS), 22(1), pp. 89–114, 2004.




  1. M. Holsheimer, M. Kersten, H. Mannila, and H. Toivonen. A perspective on databases and data mining, ACM KDD Conference, pp. 150–155, 1995.




  1. S. Hofmeyr, S. Forrest, and A. Somayaji. Intrusion detection using sequences of system calls. Journal of Computer Security, 6(3), pp. 151–180, 1998.




  1. D. Hosmer Jr., S. Lemeshow, and R. Sturdivant. Applied logistic regression. Wiley, 2013.




  1. J. Huan, W. Wang, and J. Prins. Efficient mining of frequent subgraphs in the presence of isomorphism. IEEE ICDM Conference, pp. 549–552, 2003.




  1. Z. Huang, X. Li, and H. Chen. Link prediction approach to collaborative filtering.




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