Data Mining: The Textbook



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1-Data Mining tarjima

Social Network Data Analytics, Springer, pp. 115–148. 2011.



  1. M. Bilenko, S. Basu, and R. J. Mooney. Integrating constraints and metric learning in semi-supervised clustering. ICML Conference, 2004.




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ICML Conference, 2001.



  1. C. Bohm, K. Haegler, N. Muller, and C. Plant. Coco: coding cost for parameter free outlier detection. ACM KDD Conference, 2009.




  1. K. Borgwardt, and H.-P. Kriegel. Shortest-path kernels on graphs. IEEE International Conference on Data Mining, 2005.




  1. S. Boriah, V. Chandola, and V. Kumar. Similarity measures for categorical data: A comparative evaluation. SIAM Conference on Data Mining, 2008.




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BIBLIOGRAPHY

701




  1. J. Boulicaut, A. Bykowski, and C. Rigotti. Approximation of frequency queries by means of free-sets. Principles of Data Mining and Knowledge Discovery, pp. 75–85, 2000.




  1. P. Bradley, and U. Fayyad. Refining initial points for k-means clustering. ICML Con-ference, pp. 91–99, 1998.




  1. M. Breunig, H.-P. Kriegel, R. Ng, and J. Sander. LOF: Identifying density-based local outliers. ACM SIGMOD Conference, 2000.




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  1. L. Breiman. Random forests. Machine Learning, 45(1), pp. 5–32, 2001.




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  1. S. Brin, and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer Networks, 30(1–7), pp. 107–117, 1998.




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  1. Y. Bu, L. Chen, A. W.-C. Fu, and D. Liu. Efficient anomaly monitoring over moving object trajectory streams. ACM KDD Conference, pp. 159–168, 2009.




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  1. H. Bunke, and K. Shearer. A graph distance metric based on the maximal common subgraph.Pattern recognition letters, 19(3), pp. 255–259, 1998.




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  1. D. Burdick, M. Calimlim, and J. Gehrke. MAFIA: A maximal frequent itemset algo-rithm for transactional databases. IEEE International Conference on Data Engineer-ing, pp. 443–452, 2001.




  1. C. Burges. A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2), pp. 121–167, 1998.




  1. T. Calders, and B. Goethals. Mining all non-derivable frequent itemsets. Principles of Knowledge Discovery and Data Mining, pp. 74–86, 2002.

702 BIBLIOGRAPHY





  1. T. Calders, C. Rigotti, and J. F. Boulicaut. A survey on condensed representations for frequent sets. In Constraint-based mining and inductive databases, pp. 64–80, Springer, 2006.




  1. S. Chakrabarti. Mining the Web: Discovering knowledge from hypertext data. Morgan Kaufmann, 2003.




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  1. S. Chakrabarti, S. Sarawagi, and B. Dom. Mining surprising patterns using temporal description length. VLDB Conference, pp. 606–617, 1998.




  1. K. P. Chan, and A. W. C. Fu. Efficient time series matching by wavelets.IEEE Inter-national Conference on Data Engineering, pp. 126–133, 1999.




  1. V. Chandola, A. Banerjee, and V. Kumar. Anomaly detection: A survey. ACM Com-puting Surveys, 41(3), 2009.




  1. V. Chandola, A. Banerjee, and V. Kumar. Anomaly detection for discrete sequences: A survey. IEEE Transactions on Knowledge and Data Engineering, 24(5), pp. 823–839, 2012.




  1. O. Chapelle. Training a support vector machine in the primal. Neural Computation, 19(5), pp. 1155–1178, 2007.




  1. C. Chatfield. The analysis of time series: an introduction. CRC Press, 2003.




  1. A. Chaturvedi, P. Green, and J. D. Carroll. K-modes clustering, Journal of Classifi-cation, 18(1), pp. 35–55, 2001.




  1. N. V. Chawla, N. Japkowicz, and A. Kotcz. Editorial: Special issue on learning from imbalanced data sets. ACM SIGKDD Explorations Newsletter, 6(1), 1–6, 2004.




  1. N. V. Chawla, K. W. Bower, L. O. Hall, and W. P. Kegelmeyer. SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research (JAIR), 16, pp. 321–356, 2002.




  1. N. Chawla, A. Lazarevic, L. Hall, and K. Bowyer. SMOTEBoost: Improving prediction of the minority class in boosting. PKDD, pp. 107–119, 2003.




  1. N. V. Chawla, D. A. Cieslak, L. O. Hall, and A. Joshi. Automatically countering imbalance and its empirical relationship to cost. Data Mining and Knowledge Discov-ery, 17(2), pp. 225–252, 2008.




  1. K. Chen, and L. Liu. A survey of multiplicative perturbation for privacy-preserving data mining. Privacy-Preserving Data Mining: Models and Algorithms, Springer, pp. 157–181, 2008.




  1. L. Chen, and R. Ng. On the marriage of Lp-norms and the edit distance. VLDB Conference, pp. 792–803, 2004.




  1. W. Chen, Y. Wang, and S. Yang. Efficient influence maximization in social networks. ACM KDD Conference, pp. 199–208, 2009.


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