Direct mining of discriminative patterns for classifying uncertain data

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2010, Pages 861-870

  Gao, Chuancong ^a   Wang, Jianyong ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Associative classification; Expected confidence; Frequent pattern mining; Uncertain data

Indexed keywords

ASSOCIATIVE CLASSIFICATION; CATEGORICAL DATA; CATEGORICAL DATASETS; CLASSIFICATION ACCURACY; DATA SETS; EMPIRICAL RESULTS; EXPECTED CONFIDENCE; FEATURE SELECTION; FREQUENT PATTERN MINING; FREQUENT PATTERNS; MINIMUM SUPPORT; NOVEL ALGORITHM; RULE-BASED CLASSIFIER; SVM CLASSIFIERS; TRAINING FEATURES; UNCERTAIN DATAS;

ALGORITHMS; CLASSIFIERS; DATA MINING; DECISION TREES; DISTRIBUTION FUNCTIONS; FEATURE EXTRACTION; UNCERTAINTY ANALYSIS;

CLASSIFICATION (OF INFORMATION);

EID: 77956194153     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1835804.1835913     Document Type: Conference Paper

References (22)

1. C. C. Aggarwal, Y. Li, J. Wang, and J. Wang. Frequent pattern mining with uncertain data. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 29-38, Paris, France, 2009. ACM.
2. T. Bernecker, H.-P. Kriegel, M. Renz, F. Verhein, and A. Züfle. Probabilistic frequent itemset mining in uncertain databases. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 119-128, Paris, France, 2009. ACM.
3. H. Cheng, X. Yan, J. Han, and C.-W. Hsu. Discriminative frequent pattern analysis for effective classification. In Proceedings of the 23rd International Conference on Data Engineering, pages 716-725, Istanbul, Turkey, 2007. IEEE.
4. H. Cheng, X. Yan, J. Han, and P. S. Yu. Direct discriminative pattern mining for effective classification. In Proceedings of the 24th International Conference on Data Engineering, pages 169-178, Cancún, México, 2008. IEEE.
5. C. K. Chui and B. Kao. A decremental approach for mining frequent itemsets from uncertain data. In Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008, pages 64-75, Osaka, Japan, 2008. Springer.
6. C. K. Chui, B. Kao, and E. Hung. Mining frequent itemsets from uncertain data. In Proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, pages 47-58, Nanjing, China, 2007. Springer.
7. W. W. Cohen. Fast effective rule induction. In Proceedings of the Twelfth International Conference on Machine Learning (ICML 1995), pages 115-123, Tahoe City, California, USA, 1995.
8. G. Cong, K.-L. Tan, A. K. H. Tung, and X. Xu. Mining top-k covering rule groups for gene expression data. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 670-681, Baltimore, Maryland, USA, 2005. ACM.
9. W. Fan, K. Zhang, H. Cheng, J. Gao, X. Yan, J. Han, P. S. Yu, and O. Verscheure. Direct mining of discriminative and essential frequent patterns via model-based search tree. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 230-238, Las Vegas, Nevada, USA, 2008. ACM.
10. U. M. Fayyad and K. B. Irani. Multi-interval discretization of continuous-valued attributes for classification learning. In IJCAI-93, Proceedings of the 8th International Joint Conference on Artificial Intelligence, pages 1022-1029, Chambery, France, 1993.
11. C. Gao and J. Wang. Efficient itemset generator discovery over a stream sliding window. In Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pages 355-364, Hong Kong, China, 2009. ACM.
12. J. Han, J. Pei, Y. Yin, and R. Mao. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Discov., 8(1):53-87, 2004.
13. C. K.-S. Leung, C. L. Carmichael, and B. Hao. Efficient mining of frequent patterns from uncertain data. In Workshops Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), pages 489-494, Omaha, Nebraska, USA, 2007. IEEE Computer Society.
14. W. Li, J. Han, and J. Pei. Cmar: Accurate and efficient classification based on multiple class-association rules. In Proceedings of the 2001 IEEE International Conference on Data Mining, pages 369-376, San Jose, California, USA, 2001. IEEE Computer Society.
15. B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. In Proceedings of the Fourteen ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 80-86, New York City, New York, USA, 1998.
16. B. Qin, Y. Xia, and F. Li. Dtu: A decision tree for uncertain data. In Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009, pages 4-15, Bangkok, Thailand, 2009. Springer.
17. B. Qin, Y. Xia, S. Prabhakar, and Y.-C. Tu. A rule-based classification algorithm for uncertain data. In Proceedings of the 25th International Conference on Data Engineering, ICDE 2009, pages 1633-1640, Shanghai, China, 2009. IEEE.
18. J. R. Quinlan. C4.5: Programs for Machine Learning, Morgan Kaufmann: 1 ed. 1993.
19. J. R. Quinlan and R. M. Cameron-Jones. Foil: A midterm report. In Machine Learning: ECML-93, European Conference on Machine Learning, pages 3-20, Vienna, Austria, 1993. Springer.
20. S. Tsang, B. Kao, K. Y. Yip, W.-S. Ho, and S. D. Lee. Decision trees for uncertain data. In Proceedings of the 25th International Conference on Data Engineering, ICDE 2009, pages 441-444, Shanghai, China, 2009. IEEE.
21. J. Wang and G. Karypis. On mining instance-centric classification rules. IEEE Trans. Knowl. Data Eng., 18(11):1497-1511, 2006.
22. X. Yin and J. Han. Cpar: Classification based on predictive association rules. In Proceedings of the Third SIAM International Conference on Data Mining, San Francisco, CA, USA, 2003. SIAM.