Databases Reference
In-Depth Information
8. Lin, T.Y. and Ohsuga, S. (eds.),
Proceedings of IEEE ICDM'02 Workshop on
Foundation of Data Mining and Knowledge Discovery
, 2002.
9. Lin, T.Y., Yao, Y.Y. and Louie, E., Value added association rules,
Proceedings
of PAKDD
, 328-333, 2002.
10. Mannila, H., Methods and problems in data mining,
Proceedings of International
Conference on Database Theory
, 41-55, 1997.
11. Mannila, H., Theoretical frameworks for data mining,
SIGKDD Explorations
,
1
, 30-32, 2000.
12. Martella, R.C., Nelson, R. and Marchand-Martella, N.E.,
Research Methods:
Learning to Become a Critical Research Consumer
, Allyn & Bacon, Bosten,
1999.
13. Peikoff, L.,
Objectivism: The Philosophy of Ayn Rand
, Dutton, 1991.
14. Posner, M.I. (ed.),
Foundations of Cognitive Science
, Preface: learning cognitive
science, MIT, Cambridge, Massachusetts, 1989.
15. Reif, F. and Heller, J.I., Knowledge structure and problem solving in physics,
Educational Psychologist
,
17
, 102-127, 1982.
16. Salthe, S.N.,
Evolving Hierarchical Systems, their Structure and Representation
,
Columbia University Press, New York, 1985.
17. Silberschatz, A. and Tuzhilin, A., On subjective measures of interestingness in
knowledge discovery,
Proceedings of KDD
, 275-281, 1995.
18. Silberschatz, A. and Tuzhilin, A., What makes patterns interesting in knowledge
discovery systems?
IEEE Transactions on Knowledge and Data Engineering
,
8
,
970-974, 1996.
19. Simpson, S.G., What is foundations of mathematics? 1996.
http://www.math.psu.edu/simpson/hierarchy.html,
retrieved
November
21,
2003.
20. Sowa, J.F.,
Conceptual Structures, Information Processing in Mind and Ma-
chine
, Addison-Wesley, Reading, Massachusetts, 1984.
21. Yao, Y.Y., Modeling data mining with granular computing,
Proceedings of
the 25th Annual International Computer Software and Applications Conference
(COMPSAC 2001)
, 638-643, 2001.
22. Yao, Y.Y., A step towards the foundations of data mining,
Proceedings of the
SPIE: Data Mining and Knowledge Discovery: Theory, Tools, and Technology
,
5098, 254-263, 2003.
23. Yao, Y.Y., Chen, Y.H. and Yang X.D., Measurement-theoretic foundation for
rules interestingness,
ICDM 2003 Workshop on Foundations of Data Mining
,
221-227, 2003.
24. Yao, Y.Y. and Liau, C.J., A generalized decision logic language for granular
computing,
FUZZ-IEEE on Computational Intelligence
, 1092-1097, 2002.
25. Yao, Y.Y. and Zhao, Y., Explanation-oriented data mining, in: Wang, J. (ed.),
Encyclopedia of Data Warehousing and Mining
, 492-297, Idea Group Inc., 2005.
26. Yao, Y.Y., Zhao, Y. and Maguire, R.B., Explanation-oriented association mining
using rough set theory,
Proceedings of the 9th International Conference of Rough
Sets, Fuzzy Sets, Data Mining, and Granular Computing
, 165-172, 2003.
27. Yao, Y.Y. and Zhong, N., An analysis of quantitative measures associated with
rules,
Proceedings of PAKDD'99
, 479-488, 1999.
28. Yao, Y.Y., Zhong, N. and Ohshima, M., An analysis of peculiarity oriented
multi-database mining,
IEEE Transactions on Knowledge and Data Engineer-
ing
,
15
, 952-960, 2003.
