Data Mining: The Textbook



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1-Data Mining tarjima

Lingvisticae Investigationes, 30(1), 3–26, 2007.



  1. M. Naor, and B. Pinkas. Efficient oblivious transfer protocols. SODA Conference,




    1. 448–457, 2001.




  1. A. Narayanan, and V. Shmatikov. How to break anonymity of the netflix prize dataset. arXiv preprint cs/0610105, 2006. http://arxiv.org/abs/cs/0610105




  1. G. Nemhauser, and L. Wolsey. Integer and combinatorial optimization. Wiley, New York, 1988.




  1. J. Neville, and D. Jensen. Iterative classification in relational data. AAAI Workshop on Learning Statistical Models from Relational Data, pp. 13–20, 2000.




  1. A. Ng, M. Jordan, and Y. Weiss. On spectral clustering analysis and an algorithm.



Advances in Neural Information Processing Systems, pp. 849–856, 2001.



  1. R. T. Ng, L. V. S. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained associations rules. ACM SIGMOD Conference, pp. 13– 24, 1998.




  1. R. T. Ng, and J. Han. CLARANS: A method for clustering objects for spatial data mining. IEEE Transactions on Knowledge and Data Engineering, 14(5), pp. 1003– 1016, 2002.




  1. M. Neuhaus, and H. Bunke. Automatic learning of cost functions for graph edit dis-tance. Information Sciences, 177(1), pp. 239–247, 2007.




  1. M. Neuhaus, K. Riesen, and H. Bunke. Fast suboptimal algorithms for the computa-tion of graph edit distance. Structural, Syntactic, and Statistical Pattern Recognition,

    1. 163–172, 2006.

718 BIBLIOGRAPHY





  1. K. Nigam, A. McCallum, S. Thrun, and T. Mitchell. Text classification with labeled and unlabeled data using EM. Machine Learning, 39(2), pp. 103–134, 2000.




  1. B. Ozden, S. Ramaswamy, and A. Silberschatz. Cyclic association rules. International Conference on Data Engineering, pp. 412–421, 1998.




  1. L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank citation engine: Bring-ing order to the web. Technical Report, 1999–0120, Computer Science Department, Stanford University, 1998.




  1. F. Pan, G. Cong, A. Tung, J. Yang, and M. Zaki. CARPENTER: Finding closed patterns in long biological datasets. ACM KDD Conference, pp. 637–642, 2003.




  1. T. Palpanas. Real-time data analytics in sensor networks. Managing and Mining Sen-sor Data, pp. 173–210, Springer, 2013.




  1. F. Pan, A. K. H. Tung, G. Cong, and X. Xu. COBBLER: Combining column and row enumeration for closed pattern discovery. International Conference on Scientific and Statistical Database Management, pp. 21–30, 2004.




  1. C. Papadimitriou, H. Tamaki, P. Raghavan, and S. Vempala. Latent semantic index-ing: A probabilistic analysis. ACM PODS Conference, pp. 159–168, 1998.




  1. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. International Conference on Database Theory, pp. 398–416, 1999.




  1. P. Patel, E. Keogh, J. Lin, and S. Lonardi. Mining motifs in massive time series databases. IEEE ICDM Conference, pp. 370–377, 2002.




  1. J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang. H-mine: Hyper-structure mining of frequent patterns in large databases. IEEE ICDM Conference, pp. 441–448, 2001.




  1. J. Pei, J. Han, and R. Mao. CLOSET: An efficient algorithm for mining frequent closed itemsets. ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp, 21–30, 2000.




  1. J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, and M. C. Hsu. Mining sequential patterns by pattern-growth: The prefixspan approach. IEEE Transactions on Knowledge and Data Engineering, 16(11), pp. 1424–1440, 2004.




  1. J. Pei, J. Han, and L. V. S. Lakshmanan. Mining frequent patterns with convertible constraints. ICDE Conference, pp. 433–442, 2001.




  1. D. Pelleg, and A. W. Moore. X-means: Extending k-means with efficient estimation of the number of clusters. ICML Conference, pp. 727–734, 2000.




  1. M. Petrou, and C. Petrou. Image processing: the fundamentals. Wiley, 2010.




  1. D. Pierrakos, G. Paliouras, C. Papatheodorou, and C. Spyropoulos. Web usage mining as a tool for personalization: a survey. User Modeling and User-Adapted Interaction, 13(4), pp, 311–372, 2003.




  1. D. Pokrajac, A. Lazerevic, and L. Latecki. Incremental local outlier detection for data streams. Computational Intelligence and Data Mining Conference, pp. 504–515, 2007.


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