Using Association Rules for Classification from Databases Having Class Label Ambiguities: A Belief Theoretic Method - Data Mining: Foundations and Practice - page 543

Acknowledgment
This work is based upon work supported by NSF Grants IIS-0325260 (ITR
Medium), IIS-0513702 and EAR-0323213. The work of J. Zhang was con-
ducted while he was at the University of Miami.
References
1. R. Agrawal, T. Imielinski, and A. N. Swami. Mining association rules between
sets of items in large databases. In Proceedings of ACM SIGMOD International
Conference on Management of Data , pages 207-216, Washington DC, May 1993
2. R. Agrawal and R. Srikant. Fast algorithms for mining association rules in
large databases. In Proceedings of International Conference on Very Large Data
Bases (VLDB'94) , pages 487-499, Santiago de Chile, Chile, September 1994
3. C. L. Blake and C. J. Merz. UCI repository of machine learning databases, 1998
4. T. M. Cover and P. E. Hart. Nearest neighbor pattern classification. IEEE
Transactions on Information Theory , 13(1):21-27, January 1967
5. T. Denoeux. The k -nearest neighbor classification rule based on Dempster-
Shafer theory. IEEE Transactions on Systems, Man and Cybernetics ,
25(5):804-813, May 1995
6. S. A. Dudani. The distance-weighted k -nearest-neighbor rule. IEEE Transac-
tions on Systems, Man and Cybernetics , 6(4):325-327, April 1976
7. S. Fabre, A. Appriou, and X. Briottet. Presentation and description of two
classification methods using data fusion on sensor management. Information
Fusion , 2:49-71, 2001
8. R. Fagin and J. Y. Halpern. A new approach to updating beliefs. In
P. P. Bonissone, M. Henrion, L. N. Kanal, and J. F. Lemmer, editors, Pro-
ceedings of Conference on Uncertainty in Artificial Intelligence (UAI'91) , pages
347-374. Elsevier Science, New York, NY, 1991
9. E. Fix and J. L. Hodges. Discriminatory analysis: nonparametric discrimination:
consistency properties. Technical Report 4, USAF School of Aviation Medicine,
Randolph Field, TX, 1951
10. S. L. Hegarat-Mascle, I. Bloch, and D. Vidal-Madjar. Introduction of neighbor-
hood information in evidence theory and application to data fusion of radar and
optical images with partial cloud cover. Pattern Recognition , 31(11):1811-1823,
November 1998
11. K. K. R. G. K. Hewawasam, K. Premaratne, M.-L. Shyu, and S. P. Subasingha.
Rule mining and classification in the presence of feature level and class label
ambiguities. In K. L. Priddy, editor, Intelligent Computing: Theory and Appli-
cations III , volume 5803 of Proceedings of SPIE , pages 98-107. March 2005
12. K. K. R. G. K. Hewawasam, K. Premaratne, S. P. Subasingha, and M.-L. Shyu.
Rule mining and classification in imperfect databases. In Proceedings of Inter-
national Conference on Information Fusion (ICIF'05) , Philadelphia, PA, July
2005
13. H.-J. Huang and C.-N. Hsu. Bayesian classification for data from the same
unknown class. IEEE Transactions on Systems, Man and Cybernetics, Part B:
Cybernetics , 32(2):137-145, April 2002