Data Mining: The Textbook
Yüklə 17,13 Mb.
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ACM/IEEE-CS joint conference on Digital libraries, pp. 141–142, 2005.
Z. Huang, and M. Ng. A fuzzy k-modes algorithm for clustering categorical data. IEEE Transactions on Fuzzy Systems, 7(4), pp. 446–452, 1999. G. Hulten, L. Spencer, and P. Domingos. Mining time-changing data streams. ACM KDD Conference, pp. 97–106, 2001. J. W. Hunt, and T. G. Szymanski. A fast algorithm for computing longest common subsequences. Communications of the ACM, 20(5), pp. 350–353, 1977. Y. S. Hwang, and S. Y. Bang. An efficient method to construct a radial basis function neural network classifier. Neural Networks, 10(8), pp. 1495–1503, 1997. A. Inokuchi, T. Washio, and H. Motoda. An apriori-based algorithm on mining fre-quent substructures from graph data. Principles on Knowledge Discovery and Data Mining, pp. 13–23, 2000. H. V. Jagadish, A. O. Mendelzon, and T. Milo. Similarity-based queries. ACM PODS Conference, pp. 36–45, 1995. A. K. Jain, and R. C. Dubes. Algorithms for clustering data. Prentice-Hall, Inc., 1998. A. Jain, M. Murty, and P. Flynn. Data clustering: A review. ACM Computing Surveys (CSUR), 31(3):264–323, 1999. A. Jain, R. Duin, and J. Mao. Statistical pattern recognition: A review. IEEE Trans-actions on Pattern Analysis and Machine Intelligence,, 22(1), pp. 4–37, 2000.
V. Janeja, and V. Atluri. Random walks to identify anomalous free-form spatial scan windows. IEEE Transactions on Knowledge and Data Engineering, 20(10), pp. 1378– 1392, 2008. J. Rennie, and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. ICML Conference, pp. 713–718, 2005. G. Jeh, and J. Widom. SimRank: a measure of structural-context similarity. ACM KDD Conference, pp. 538–543, 2003. H. Jeung, M. L. Yiu, X. Zhou, C. Jensen, and H. Shen. Discovery of convoys in trajectory databases. VLDB Conference, pp. 1068–1080, 2008. T. Joachims. Making Large scale SVMs practical. Advances in Kernel Methods, Sup-port Vector Learning, pp. 169–184, MIT Press, Cambridge, 1998. T. Joachims. Training Linear SVMs in Linear Time. ACM KDD Conference, pp. 217– 226, 2006. T. Joachims. Transductive inference for text classification using support vector machines. International Conference on Machine Learning, pp. 200–209, 1999. T. Joachims. Transductive learning via spectral graph partitioning. ICML Conference, 290–297, 2003. I. Jolliffe. Principal component analysis. John Wiley and Sons, 2005. M. Joshi, V. Kumar, and R. Agarwal. Evaluating boosting algorithms to classify rare classes: comparison and improvements. IEEE ICDM Conference, pp. 257–264, 2001. M. Kantarcioglu. A survey of privacy-preserving methods across horizontally par-titioned data. Privacy-Preserving Data Mining: Models and Algorithms, Springer, 313–335, 2008. H. Kashima, K. Tsuda, and A. Inokuchi. Kernels for graphs. In Kernel Methods in Computational Biology, MIT Press, Cambridge, MA, 2004. D. Karger, and C. Stein. A new approach to the minimum cut problem. Journal of the ACM (JACM), 43(4), pp. 601–640, 1996. G. Karypis, E. H. Han, and V. Kumar. Chameleon: Hierarchical clustering using dynamic modeling. Computer, 32(8), pp, 68–75, 1999. G. Karypis, and V. Kumar. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on scientific Computing, 20(1), pp. 359–392, 1998. G. Karypis, R. Aggarwal, V. Kumar, and S. Shekhar. Multilevel hypergraph partition-ing: applications in VLSI domain. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 7(1), pp. 69–79, 1999. L. Kaufman, and P. J. Rousseeuw. Finding groups in data: an introduction to cluster analysis. Wiley, 2009. D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. ACM KDD Conference, pp. 137–146, 2003. 712 BIBLIOGRAPHY E. Keogh, S. Lonardi, and C. Ratanamahatana. Towards parameter-free data mining. ACM KDD Conference, pp. 206–215, 2004. E. Keogh, J. Lin, and A. Fu. HOT SAX: Finding the most unusual time series subse- quence: Algorithms and applications. IEEE ICDM Conference, pp. 8, 2005. E. Keogh, and M. Pazzani. Scaling up dynamic time-warping for data mining appli-cations. ACM KDD Conference, pp. 285–289, 2000. E. Keogh. Exact indexing of dynamic time warping. VLDB Conference, pp. 406–417, 2002. E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra. Dimensionality reduction for fast similarity searching in large time series datanases. Knowledge and Infomration Systems, pp. 263–286, 2000. E. Keogh, S. Lonardi, and B. Y.-C. Chiu. Finding surprising patterns in a time series database in linear time and space. ACM KDD Conference, pp. 550–556, 2002. E. Keogh, S. Lonardi, and C. Ratanamahatana. Towards parameter-free data mining. ACM KDD Conference, pp. 206–215, 2004. B. Kernighan, and S. Lin. An efficient heuristic procedure for partitioning graphs. Yüklə 17,13 Mb. Dostları ilə paylaş:
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