-
W. Chen, C. Wang, and Y. Wang. Scalable influence maximization for prevalent viral marketing in large-scale social networks. ACM KDD Conference, pp. 1029–1038, 2010.
-
W. Chen, Y. Yuan, and L. Zhang. Scalable influence maximization in social networks under the linear threshold model. IEEE International Conference on Data Mining,
-
88–97, 2010.
-
D. Chen, C.-T. Lu, Y. Chen, and D. Kou. On detecting spatial outliers. Geoinformat-ica, 12: pp. 455–475, 2008.
-
T. Cheng, and Z. Li. A hybrid approach to detect spatialtemporal outliers. Interna-tional Conference on Geoinformatics, pp. 173–178, 2004.
-
T. Cheng, and Z. Li. A multiscale approach for spatio-temporal outlier detection. Transactions in GIS, 10(2), pp. 253–263, March 2006.
-
Y. Cheng. Mean shift, mode seeking, and clustering. IEEE Transactions on PAMI, 17(8), pp. 790–799, 1995.
-
H. Cheng, X. Yan, J. Han, and C. Hsu. Discriminative frequent pattern analysis for effective classification. ICDE Conference, pp. 716–725, 2007.
-
F. Y. Chin, and G. Ozsoyoglu. Auditing and inference control in statistical databases.
IEEE Transactions on Software Enginerring, 8(6), pp. 113–139, April 1982.
-
B. Chiu, E. Keogh, and S. Lonardi. Probabilistic discovery of time series motifs. ACM KDD Conference, pp. 493–498, 2003.
-
F. Chung. Spectral Graph Theory. Number 92 in CBMS Conference Series in Math-ematics, American Mathematical Society, 1997.
-
V. Ciriani, S. De Capitani di Vimercati, S. Foresti, and P. Samarati. k-anonymous data mining: A survey. Privacy-preserving data mining: models and algorithms, Springer,
-
105–136, 2008.
-
C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, and M. Y. Zhu. Tools for privacy preserving distributed data mining. ACM SIGKDD Explorations Newsletter, 4(2),
-
28–34, 2002.
-
N. Cristianini, and J. Shawe-Taylor. An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, 2000.
-
W. Cochran. Sampling techniques. John Wiley and Sons, 2007.
-
D. Cohn, L. Atlas, and R. Ladner. Improving generalization with active learning. Machine Learning, 5(2), pp. 201–221, 1994.
-
D. Cohn, Z. Ghahramani, and M. Jordan. Active learning with statistical models.
Journal of Artificial Intelligence Research, 4, pp. 129–145, 1996.
-
D. Comaniciu, and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on PAMI, 24(5), pp. 603–619, 2002.
-
D. Cook, and L. Holder. Graph-based data mining. IEEE Intelligent Systems, 15(2),
-
32–41, 2000.
704 BIBLIOGRAPHY
-
R. Cooley, B. Mobasher, and J. Srivastava. Data preparation for mining world wide web browsing patterns. Knowledge and information systems, 1(1), pp. 5–32, 1999.
-
L. P. Cordella, P. Foggia, C. Sansone, and M. Vento. A (sub)graph isomorphism algo-rithm for matching large graphs. IEEE Transactions on Pattern Mining and Machine Intelligence, 26(10), pp. 1367–1372, 2004.
-
H. Shang, Y. Zhang, X. Lin, and J. X. Yu. Taming verification hardness: an efficient algorithm for testing subgraph isomorphism. Proceedings of the VLDB Endowment, 1(1), pp. 364–375, 2008.
-
J. R. Ullmann. An algorithm for subgraph isomorphism. Journal of the ACM, 23:
-
31–42, January 1976.
-
G. Cormode, and S. Muthukrishnan. An improved data stream summary: the count-min sketch and its applications. Journal of Algorithms, 55(1), pp. 58–75, 2005.
-
S. Cost, and S. Salzberg. A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning, 10(1), pp. 57–78, 1993.
-
T. Cover, and P. Hart. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), pp. 21–27, 1967.
-
D. Cutting, D. Karger, J. Pedersen, and J. Tukey. Scatter/gather: A cluster-based approach to browsing large document collections. ACM SIGIR Conference, pp. 318– 329, 1992.
-
M. Dash, K. Choi, P. Scheuermann, and H. Liu. Feature selection for clustering-a filter solution. ICDM Conference, pp. 115–122, 2002.
-
M. Deshpande, and G. Karypis. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS), 22(1), pp. 143–177, 2004.
-
I. Dhillon. Co-clustering documents and words using bipartite spectral graph parti-tioning, ACM KDD Conference, pp. 269–274, 2001.
-
I. Dhillon, S. Mallela, and D. Modha. Information-theoretic co-clustering. ACM KDD Conference, pp. 89–98, 2003.
-
I. Dhillon, Y. Guan, and B. Kulis. Kernel k-means: spectral clustering and normalized cuts. ACM KDD Conference, pp. 551–556, 2004.
-
P. Domingos. MetaCost: A general framework for making classifiers cost-sensitive. ACM KDD Conference, pp. 155–164, 1999.
-
P. Domingos. Bayesian averaging of classifiers and the overfitting problem. ICML Conference, pp. 223–230, 2000.
-
P. Domingos, and G. Hulten. Mining high-speed data streams. ACM KDD Conference,
-
71–80. 2000.
-
P. Clark, and T. Niblett. The CN2 induction algorithm. Machine Learning, 3(4),
-
261–283, 1989.
-
W. W. Cohen. Fast effectve rule induction. ICML Conference, pp. 115–123, 1995.
Dostları ilə paylaş: |