Data Mining: The Textbook



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1-Data Mining tarjima

ECML Conference, pp. 4–15, 1998.



  1. D. Lewis, and J. Catlett. Heterogeneous uncertainty sampling for supervised learning. ICML Conference, pp. 148–156, 1994.




  1. C. Li, Q. Yang, J. Wang, and M. Li. Efficient mining of gap-constrained subsequences and its various applications. ACM Transactions on Knowledge Discovery from Data (TKDD), 6(1), 2, 2012.




  1. J. Li, G. Dong, K. Ramamohanarao, and L. Wong. Deeps: A new instance-based lazy discovery and classification system. Machine Learning, 54(2), pp. 99–124, 2004.




  1. N. Li, T. Li, and S. Venkatasubramanian. t-closeness: Privacy beyond k-anonymity and -diversity. IEEE International Conference on Data Engineering, pp. 106–115, 2007.




  1. W. Li, J. Han, and J. Pei. CMAR: Accurate and efficient classification based on multiple class-association rules. IEEE ICDM Conference, pp. 369–376, 2001.




  1. Y. Li, M. Dong, and J. Hua. Localized feature selection for clustering. Pattern Recog-nition Letters, 29(1), 10–18, 2008.




  1. Z. Li, B. Ding, J. Han, and R. Kays. Swarm: Mining relaxed temporal moving object clusters. Proceedings of the VLDB Endowment, 3(1–2), pp. 732–734, 2010.




  1. Z. Li, B. Ding, J. Han, R. Kays, and P. Nye. Mining periodic behaviors for moving objects. ACM KDD Conference, pp. 1099–1108, 2010.




  1. D. Liben-Nowell, and J. Kleinberg. The link-prediction problem for social networks.



Journal of the American Society for Information Science and Technology, 58(7), pp. 1019–1031, 2007.



  1. R. Lichtenwalter, J. Lussier, and N. Chawla. New perspectives and methods in link prediction. ACM KDD Conference, pp. 243–252, 2010.




  1. J. Lin, E. Keogh, S. Lonardi, and B. Chiu. Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), pp. 107– 144, 2003.




  1. J. Lin, E. Keogh, S. Lonardi, and P. Patel. Finding motifs in time series. Proceedings of the 2nd Workshop on Temporal Data, 2002.

BIBLIOGRAPHY

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  1. B. Liu. Web data mining: exploring hyperlinks, contents, and usage data. Springer, New York, 2007.




  1. B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining.



ACM KDD Conference, pp. 80–86, 1998.



  1. G. Liu, H. Lu, W. Lou, and J. X. Yu. On computing, storing and querying frequent patterns. ACM KDD Conference, pp. 607–612, 2003.




  1. H. Liu, and H. Motoda. Feature selection for knowledge discovery and data mining.



Springer, 1998.



  1. J. Liu, Y. Pan, K. Wang, and J. Han. Mining frequent item sets by opportunistic projection. ACM KDD Conference, pp. 229–238, 2002.




  1. L. Liu, J. Tang, J. Han, M. Jiang, and S. Yang. Mining topic-level influence in het-erogeneous networks. ACM CIKM Conference, pp. 199–208, 2010.




  1. D. Lin. An Information-theoretic Definition of Similarity. ICML Conference, pp. 296– 304, 1998.




  1. R. Little, and D. Rubin. Statistical analysis with missing data. Wiley, 2002.




  1. F. T. Liu, K. M. Ting, and Z.-H. Zhou. Isolation forest. IEEE ICDM Conference,




    1. 413–422, 2008.




  1. H. Liu, and H. Motoda. Computational methods of feature selection. Chapman and Hall/CRC, 2007.




  1. K. Liu, C. Giannella, and H. Kargupta. A survey of attack techniques on privacy-preserving data perturbation methods. Privacy-Preserving Data Mining: Models and Algorithms, Springer, pp. 359–381, 2008.




  1. B. London, and L. Getoor. Collective classification of network data. Data Classifica-tion: Algorithms and Applications, CRC Press, pp. 399–416, 2014.




  1. C.-T. Lu, D. Chen, and Y. Kou. Algorithms for spatial outlier detection, IEEE ICDM Conference, pp. 597–600, 2003.




  1. Q. Lu, and L. Getoor. Link-based classification. ICML Conference, pp. 496–503, 2003.




  1. U. von Luxburg. A tutorial on spectral clustering. Statistics and computing, 17(4),




    1. 395–416, 2007.




  1. A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam. -diversity: privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(3), 2007.




  1. S. Macskassy, and F. Provost. A simple relational classifier. Second Workshop on Multi-Relational Data Mining (MRDM) at ACM KDD Conference, 2003.




  1. S. C. Madeira, and A. L. Oliveira. Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 1(1), pp. 24–45, 2004.

716 BIBLIOGRAPHY





  1. N. Mamoulis, H. Cao, G. Kollios, M. Hadjieleftheriou, Y. Tao, and D. Cheung. Min-ing, indexing, and querying historical spatiotemporal data. ACM KDD Conference,

    1. 236–245, 2004.




  1. G. Manku, and R. Motwani. Approximate frequency counts over data streams. VLDB Conference, pp. 346–357, 2002.




  1. C. Manning, P. Raghavan, and H. Schutze. Introduction to information retrieval.




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