Data Mining: The Textbook



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1-Data Mining tarjima

BIBLIOGRAPHY

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  1. G. Sheikholeslami, S. Chatterjee, and A. Zhang. Wavecluster: A multi-resolution clus-tering approach for very large spatial databases. VLDB Conference, pp. 428–439, 1998.




  1. P. Shenoy, J. Haritsa, S. Sudarshan, G., Bhalotia, M. Bawa, and D. Shah. Turbo-charging vertical mining of large databases. ACM SIGMOD Conference, 29(2), pp. 22– 35, 2000.




  1. J. Shi, and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 22(8), pp. 888–905, 2000.




  1. R. Shumway, and D. Stoffer. Time-series analysis and its applications: With R exam-ples, Springer, New York, 2011.




  1. M.-L. Shyu, S.-C. Chen, K. Sarinnapakorn, and L. Chang. A novel anomaly detection scheme based on principal component classifier, ICDM Conference, pp. 353–365, 2003.




  1. R. Sibson. SLINK: An optimally efficient algorithm for the single-link clustering method. The Computer Journal, 16(1), pp. 30–34, 1973.




  1. A. Siebes, J. Vreeken, and M. van Leeuwen. itemsets that compress. SDM Conference, pp. 393–404, 2006.




  1. B. W. Silverman. Density Estimation for Statistics and Data Analysis. Chapman and Hall, 1986.




  1. K. Smets, and J. Vreeken. The odd one out: Identifying and characterising anomalies.



SIAM Conference on Data Mining, pp. 804–815, 2011.



  1. E. S. Smirnov. On exact methods in systematics. Systematic Zoology, 17(1), pp. 1–13, 1968.




  1. P. Smyth. Clustering sequences with hidden Markov models. Advances in Neural Information Processing Systems, pp. 648–654, 1997.




  1. E. J. Stollnitz, and T. D. De Rose. Wavelets for computer graphics: theory and appli-cations. Morgan Kaufmann, 1996.




  1. R. Srikant, and R. Agrawal. Mining quantitative association rules in large relational tables. ACM SIGMOD Conference, pp. 1–12, 1996.




  1. J. Srivastava, R. Cooley, M. Deshpande, and P. N. Tan. Web usage mining: Discov-ery and applications of usage patterns from web data. ACM SIGKDD Explorations Newsletter, 1(2), pp. 12–23, 2000.




  1. I. Steinwart, and A. Christmann. Support vector machines. Springer, 2008.




  1. A. Strehl, and J. Ghosh. Cluster ensembles—a knowledge reuse framework for com-bining multiple partitions. Journal of Machine Learning Research, 3, pp. 583–617, 2003.




  1. G. Strang. An introduction to linear algebra. Wellesley Cambridge Press, 2009.




  1. G. Strang, and K. Borre. Linear algebra, geodesy, and GPS. Wellesley Cambridge Press, 1997.

722 BIBLIOGRAPHY





  1. K. Subbian, C. Aggarwal, and J. Srivasatava. Content-centric flow mining for influence analysis in social streams. CIKM Conference, pp. 841–846, 2013.




  1. J. Sun, and J. Tang. A survey of models and algorithms for social influence analysis.




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