Integrating induction and deduction for noisy data mining

Information Sciences

Volumn 180, Issue 14, 2010, Pages 2663-2673

  Zhang, Yan ^a   Wu, Xindong ^a,b  

^a University of Vermont ^*  (United States)

^b HEFEI UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Deduction; Error correction; Induction; Noise handling

Indexed keywords

DATA MINING TECHNIQUES; DATA VOLUME; DEDUCTION; DEDUCTIVE REASONING; INDUCTION; INDUCTIVE LEARNING; INPUT DATAS; INTEGRATION FRAMEWORKS; NEW OPPORTUNITIES; NOISE CORRUPTION; NOISE HANDLING; NUMBER OF DATUM; STRUCTURED KNOWLEDGE;

CRIME; ERROR CORRECTION; INPUT OUTPUT PROGRAMS; INTEGRATION;

DATA MINING;

EID: 77958151233     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2009.11.045     Document Type: Article

References (43)

1. Rakesh Agrawal, Ramakrishnan Srikant, Fast algorithms for mining association rules in large databases, in: Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, 1994, pp. 487-499.
2. Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami, Mining association rules between sets of items in large databases, in: The 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, USA, 1993, pp. 207-216.
3. A. Anagnostopoulos, A. Broder, and K. Punera Effective and efficient classification on a search-engine model Knowledge and Information Systems 16 2 2008 129 154
4. I. Assent, R. Krieger, B. Glavic, and T. Seidl Clustering multidimensional sequences in spatial and temporal databases Knowledge and Information Systems 16 1 2008 29 51
5. Brad M. Barber, Terrance Odean, Ning Zhu, Systematic noise, in: AFA, San Diego, 2004.
6. L. Breiman, J. Friedman, R. Olshen, and C. Stone Classification and Regression Trees 1984 Chapman & Hall/CRC
7. G. Chen, M. Ren, P. Yan, and X. Guo Enriching the ER model based on discovered association rules Information Sciences 177 7 2007 1558 1566 (Pubitemid 46107423)
8. J. Cheng, Y. Ke, and W. Ng A survey on algorithms for mining frequent itemsets over data streams Knowledge and Information Systems 16 1 2008 1 27
9. C. Creighton, and S. Hanash Mining gene expression databases for association rules Bioinformatics 19 1 2003 79 86
10. D. Fisher Iterative optimization and simplification of hierarchical clusterings Journal of Artifical Intelligence Research 4 1996 147 179
11. C.W. Dunnet A multiple comparison procedure for comparing several treatments with a control Journal of the American Statistical Association 50 1955 1096 1121
12. M. Ester, H. Kriegel, S. Jorg, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996, pp. 226-231.
13. Peter A. Flach, and Nicolas Lachiche Confirmation-guided discovery of first-order rules with tertius Machine Learning 42 2001 61 95
14. Yoav Freund, and Robert E. Schapire A decision-theoretic generalization of on-line learning and an application to boosting Journal of Computer and System Sciences 55 1997 119 139
15. S. Greco, E. Masciari, and L. Pontieri Combining inductive and deductive tools for data analysis AI Communications 14 2 2001 69 82
16. S. Guha, R. Rastogi, K. Shim, Cure: an efficient clustering algorithm for large databases, in: Proceedings of 1998 ACM-SIGMOD International Conference Management of Data, 1998, pp. 73-84.
17. C. Hamzaebi Improving artificial neural networks performance in seasonal time series forecasting Information Sciences 178 23 2008 4550 4559
18. J. Han, J. Pei, Y. Yin, Mining frequent patterns without candidate generation, in: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 2000, pp. 1-12.
19. D.J. Hand, and K. Yu Idiots Bayes: not so stupid after all? International Statistical Review 69 1996 385 398
20. J.A. Hartigan, and M.A. Wong A k-means clustering algorithm Applied Statistics 28 1 1979 100 108
21. T. Hastie, and R. Tibshirani Discriminant adaptive nearest neighbor classification IEEE Transactions on Pattern Analysis and Machine Intelligence 18 6 1996 607 616
22. S. Hettich, S.D. Bay, The UCI KDD archive, University of California, Department of Information and Computer Science, Irvine, CA, 1999. .
23. D. Janssens, G. Wets, T. Brijs, and K. Vanhoof Adapting the CBA algorithm by means of intensity of implication Information Sciences 173 4 2005 305 318
24. M. Kanevski, R. Parkin, A. Pozdnukhov, V. Timonin, M. Maignan, V. Demyanov, and S. Canu Environmental data mining and modeling based on machine learning algorithms and geostatistics Environmental Modelling and Software 19 9 2004 845 855
25. W.C. Kim, J.Y. Song, S.W. Kim, and S. Park Image retrieval model based on weighted visual features determined by relevance feedback Information Sciences 178 22 2008 4301 4313
26. M.Y. Lin, S.C. Hsueh, and C.W. Chang Fast discovery of sequential patterns in large databases using effective time-indexing Information Sciences 178 22 2008 4228 4245
27. B. Liu, W. Hsu, Y. Ma, Integrating classification and association rule mining, in: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD98), 1998.
28. D. Luebbers, U. Grimmer, M. Jarke, Systematic development of data mining-based data quality tools, in: Proceedings of the 29th International Conference on Very Large Data Bases (VLDB-2003), Berlin, Germany, 2003, pp. 548-559.
29. G. McLachlan, and D. Peel Finite Mixture Models 2000 Wiley-Interscience
30. R. Nayak Fast and effective clustering of XML data using structural information Knowledge and Information Systems 14 2 2008 197 215
31. K. Orr Data quality and systems theory Communications of the ACM 42 2 1998 66 71
32. J.R. Quinlan C4.5: Programs for Machine Learning: Book and Software Package 1993 Morgan Kaufmann
33. F. Recknagel Applications of machine learning to ecological modeling Ecological Modelling 146 2001 1 3
34. V.N. Vapnik The Nature of Statistical Learning Theory 1995 Springer
35. X. Wu, and X. Zhu Mining with noise knowledge: error-aware data mining IEEE Transactions on Systems, Man and Cybernetics, Part A 38 4 2008 917 932
36. X. Wu, V. Kumar, J. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. McLachlan, A. Ng, B. Liu, P. Yu, Z. Zhou, M. Steinbach, D. Hand, and D. Steinberg Top 10 algorithms in data mining Knowledge and Information Systems 14 2008 1 37
37. Q. Yang, and X. Wu 10 challenging problems in data mining research International Journal of Information Technology and Decision Making 5 4 2006 597 604
38. G.W. You, and S.W. Hwangk Search structures and algorithms for personalized ranking Information Sciences 178 20 2008 3925 3942
39. T. Zhang, R. Ramakrishnan, and M. Livny Birch: an efficient data clustering method for very large databases ACM SIGMOD Record 25 2 1996 103 114
40. X. Zhu Knowledge Discovery and Data Mining: Challenges and Realities IGI Global 2007
41. X. Zhu, Semi-supervised Learning Literature Survey, Cs tr-1530, University of Wisconsin-Madison, 2008.
42. X. Zhu, X. Wu, and Q. Chen Bridging local and global data cleansing: identifying class noise in large, distributed datasets Data Mining and Knowledge Discovery 12 2 2006 275 308
43. Xingquan Zhu, Xindong Wu, Error awareness data mining, in: Proceedings of the IEEE International Conference on Granular Computing (GRC-2006), Atlanta, 2006.