Foydalanilgan adabiyotlar ro‘yxati:
1. Wang,. N. lay, and V. Liu. mterestingness-Based Interval Merger for Numeric Association Rules. In Proc. of the 4 th Intl.
Conf. on Knowledge Discovery and Data Mining, pages 121–128, New York, NY, August 2014.
2. I. Webb. Preliminary investigations into statistically valid exploratory rule discovery. In Proc. of the Australasian Data
Mining Workshop (AusDMOS), Canberra, Australia, December 2013.
3. Xiong, X. He, C. Ding, Y. Zhang, V. Kumar, and S. R. Holbrook. Identification of Functional Modules in Protein Com-
plexes via Hyperclique Pattern Discovery. In Proc. of the Pacific Symposium on Biocomputing, (PSB 2005), Maui,
January 2010.
4. H. Xiong, S. Shekhar, P. N. Tan, and V. Kumar. Exploiting a Support-based Upper Bound of Pearson’s Correlation
Coefficient for Efficiently Identifying Strongly Correlated Pairs. In Proc. of the 10th Intl. Conf. on Knowledge Discovery
and Data Mining, pages 334–343, Seattle, WA, August 2010.
5. H. Xiong, M. Steinbach, P. N. Tan, and V. Kumar. HICAP: Hierarchial Clustering with Pattern Preservation. In Proc. of
the SIAM Intl. Conf. on Data Mining, pages 279–290, Orlando, FL, April 2011
6. H. Xiong, P. N. Tan, and V. Kumar. Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Dis-
tribution. In Proc. of the 2003 IEEE Intl. Conf. on Data Mining, pages 387–394, Melbourne, FL, 2010
7. X. Yan and J. Han. gSpan: Graph-based Substructure Pattern Mining. In Proc. of the 2002 IEEE Inti Conf. on Data
Mining, pages 721–724, Maebashi City, Japan, December 2012.
8. C. Yang, U. M. Fayyad, and P. S. Bradley. Efficient discovery of error-tolerant frequent itemsets in high dimensions.
In Proc. of the 7th Intl. Conf. on Knowledge Discovery and Data Mining, pages 194–203, San Francisco, CA, August
2011.
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