Data Mining: The Textbook



Yüklə 17,13 Mb.
səhifə411/423
tarix07.01.2024
ölçüsü17,13 Mb.
#211690
1   ...   407   408   409   410   411   412   413   414   ...   423
1-Data Mining tarjima

Proceedings of the National Academy of Sciences, 99(12), pp. 7821–7826.



  1. S. Goil, H. Nagesh, and A. Choudhary. MAFIA: Efficient and scalable subspace clus-tering for very large data sets. ACM KDD Conference, pp. 443–452, 1999.




  1. D. W. Goodall. A new similarity index based on probability. Biometrics, 22(4), pp. 882–907, 1966.

708 BIBLIOGRAPHY





  1. K. Gouda, and M. J. Zaki. Genmax: An efficient algorithm for mining maximal fre-quent itemsets. Data Mining and Knowledge Discovery, 11(3), pp. 223–242, 2005.




  1. A. Goyal, F. Bonchi, and L. V. S. Lakshmanan. A data-based approach to social influence maximization. VLDB Conference, pp. 73–84, 2011.




  1. A. Goyal, F. Bonchi, and L. V. S. Lakshmanan. Learning influence probabilities in social networks. ACM WSDM Conference, pp. 241–250, 2011.




  1. R. Gozalbes, J. P. Doucet, and F. Derouin. Application of topological descriptors in QSAR and drug design: history and new trends. Current Drug Targets-Infectious Disorders, 2(1), pp. 93–102, 2002.




  1. M. Gupta, J. Gao, C. Aggarwal, and J. Han. Outlier detection for temporal data. Morgan and Claypool, 2014.




  1. S. Guha, R. Rastogi, and K. Shim. ROCK: A robust clustering algorithm for categor-ical attributes. Information Systems, 25(5), pp. 345–366, 2000.




  1. S. Guha, R. Rastogi, and K. Shim. CURE: An efficient clustering algorithm for large databases. ACM SIGMOD Conference, pp. 73–84, 1998.




  1. S. Guha, A. Meyerson, N. Mishra, R. Motwani, and L. O’Callaghan. Clustering data streams: Theory and practice. IEEE Transactions on Knowledge and Data Engineer-ing, 15(3), pp. 515–528, 2003.




  1. D. Gunopulos, and G. Das. Time series similarity measures and time series indexing.



ACM SIGMOD Conference, pp, 624, 2001.



  1. V. Guralnik, and G. Karypis. A scalable algorithm for clustering sequential data.



IEEE International Conference on Data Engineering, pp. 179–186, 2001.



  1. V. Guralnik, and G. Karypis. Parallel tree-projection-based sequence mining algo-rithms. Parallel Computing, 30(4): pp. 443–472, April 2004. Also appears in European Conference in Parallel Processing, 2001.




  1. D. Gusfield. Algorithms on strings, trees and sequences. Cambridge University Press, 1997.




  1. I. Guyon (Ed.). Feature extraction: foundations and applications. Springer, 2006.




  1. I. Guyon, and A. Elisseeff. An introduction to variable and feature selection. Journal of Machine Learning Research, 3, pp. 1157–1182, 2003.




  1. M. Halkidi, Y. Batistakis, and M. Vazirgiannis. Cluster validity methods: part I. ACM SIGMOD record, 31(2), pp. 40–45, 2002.




  1. M. Halkidi, Y. Batistakis, and M. Vazirgiannis. Clustering validity checking methods: part II. ACM SIGMOD Record, 31(3), pp. 19–27, 2002.




  1. E. Han, and G. Karypis. Centroid-based document classification: analysis and exper-imental results. ECML Conference, pp. 424–431, 2000.




  1. J. Han, M. Kamber, and J. Pei. Data mining: concepts and techniques. Morgan Kauf-mann, 2011.


Yüklə 17,13 Mb.

Dostları ilə paylaş:
1   ...   407   408   409   410   411   412   413   414   ...   423




Verilənlər bazası müəlliflik hüququ ilə müdafiə olunur ©azkurs.org 2024
rəhbərliyinə müraciət

gir | qeydiyyatdan keç
    Ana səhifə


yükləyin