Bibliography

1

Adriaans, P. and Zantinge, D. Data Mining. Addison Wesley Longman, Harlow, England, 1996

2

Agrawal, R. et al. Modeling Multidimensional Databases. Research Report, IBM Almaden Research Center, San Jose, CA, 1997

3

Agrawal, S. et al. On the Computation of Multidimensional Aggregates. Proceedings of the 22nd VLDB Conference, Bombay, India, 1996

4

Agrawal, R. et al. Mining Association Rules between Sets of Items in Large Databases. Proceedings of the 1993 ACM SIGMOD Conference, Washington, DC, May 1993

5

Boyce, W.E. and DiPrima, R.C. Elementary differential equations- 5th edition. John Wiley & Sons, Inc., New York, NY, 1986

6

Cabena, P. et. al. Discovering data mining: from concept to implementation. Prentice Hall, Inc., Upper Saddle River, NJ, 1997

7

Cavallo, R.E. and Klir, G.J. Reconstructability analysis of multi-dimensional relations: a theoretical basis for computer-aided determination of acceptable system models. International Journal of General systems, 5 (1979), 143-171

8

Christensen, R. Analysis of Variance, Design and Regression. Chapman& Hall, London, UK, 1996

9

Fayyad, U.M. Editorial. Data Mining and Knowledge Discovery, 1 (1997), 5-10

10

Fisher, R.A. The Statistical Utilization of Multiple Measurements. Annals of Eugenics, 8 (1938), 376-386

11

Glymour, C et al. Statistical Themes and Lessons for Data Mining. Data Mining and Knowledge Discovery, 1 (1997), 11-28

12

Goldberg, D.E. Genetic Algorithm in search, optimization and machine Learning. Addison-Wesley, Inc., 1989

13

Goodman, R.M. and Smyth, P. An information-theoretic model for rule-based expert systems. 1988 Int. Symposium in Information Theory, Kobe, Japan, 1988

14

Hai, A. and Klir, G.J. An empirical investigation of reconstructability analysis: probabilistic systems. International Journal of Man-Machine Studies, 22 (Feb 1985), 163-192

15

Hartley, R.V.L., Transmission of Information. Bell Systems Technical Journal, 7 (July 1928), pp. 535

16

Hosmer, D.W. and Lemeshow, S. Applied Logistic Regression. Wiley series in probability and mathematical statistics, Wiley & Sons, New York, NY, 1989

17

Efron, B. The Efficiency of Logistic Regression Compared to Normal Discriminant Analysis. Journal of the American Statistical Association, 70 (1975), 892-898

18

Han, J. Data Mining Techniques and Applications. UCLA short class, 2-5. Feb. 1998

19

Iverson, G.R. and Norpoth H. Analysis of Variance. Sage Publications, Inc., Beverly Hills, CA, 1976

20

Johnson, R.A. and Wichern, D.W. Applied Multivariate Statistical Analysis. Prentice Hall, Inc., Englewood Cliffs, NJ, 1988

21

Jones, B. K-systems versus classical multivariate systems. International Journal of General Systems, 12 (1986), 1-6

22

Jones, B. and Gouw, D. The Interaction Concept of K-Systems Theory. International Journal of General Systems, 24 (1996), 163-169

23

Joslyn, C. Towards General Information Theoretical Representations of Database Problems. Proccedings of 1997 Conference of the IEEE Society for Systems, Man, and Cybernetics.

24

Joslyn, C. Data Exploration through Extension and Projection. yet unpublished technical report, 1998

25

Klir, G.J. On sustems methodology and inductive reasoning: the issue of parts and wholes. General Systems Yearbook, 26, 29-38

26

Klir, G.J. Architecture of system problem solving. Plenum Press, New York, NY, 1985

27

Klir, G.J. and Parviz, B. General reconstruction characteristics of probabilistic and possibilistic systems. International Journal of Man-Machine Studies, 25 (Oct. 1986), 367-397

28

Klir, G.J. and Folger, T. Fuzzy Sets, Uncertainty, and Information. Prentice Hall, Englewood Cliffs, NJ, 1988

29

Klir, G.J. and Yuan, B. Fuzzy sets and fuzzy logic: theory and applications. Prentice Hall, Inc., Upper Saddle River, NJ, 1995

30

Knoke, D. and Burke, P.J. Log-Linear Models. Sage University Paper series on Quantitative Applications in the Social Sciences, series no. 07-020, Sage Publications, Beverly Hills and London, 1980

31

Lin, T.Y. and Cercone, N. Rough sets and data mining. Kluver, Norwell, MA, 1997

32

Martin, J.K. An exact probability metric for decision tree splitting and stopping. Machine Learning, 28 (1997), 257-291

33

McCulloch, W.S. and Pitts, W. A logical calculus of the ideas immanent in neurons activity. Bulletin of Mathematical Biophysics, 5 (1943), 115-133

34

Miller, I., Freund, J.E., and Johnson,R.A. Probability and statistics for engineers - 4th edition. Prentice Hall, Englewood Cliffs, NJ, 1990

35

Mitchell, M. An Introduction to Genetic Algorithms. MIT-Press, 1996

36

Motwani, R., Brin, S., and Silverstein, C. Beyond Market Baskets: Generalizing Association Rules to Correlation 1997 ACM SIGMOD Conference on Management of Data, 1997, pp. 265-276

37

Nilsson, N.J. Learning Machines: Foundations of Trainable Pattern-Classifying Systems. McGraw-Hill, New York, NY, 1965

38

Pittarelli, M. A Note on Probability Estimation using Reconstructability Analysis. International Journal of General Systems, 18 (1990), 11-21

39

Pittarelli, M. An Algebra for Probabilistic Databases. IEEE Transactions on Knowledge and data Engineering, 6 (April 1994), 293-303

40

Shannon, C.E. A Mathematical Theory of Communiction. The Bell Systems Technical Journal, 27 (1948), 379-423

41

Smyth, P. and Goodman, R.M. An Information Theoretic Approach to Rule Induction from Databases. IEEE Transactions on Knowledge and Data Engineering, 4 (Aug. 1992), 301-316

42

Piatetsky-Shapiro, G. and Frawley, W.J. (eds.). Knowledge Discovery in Databases. AAAI Press/ MIT Press, Menlo Park, CA, 1991

43

Pandya, A.S. and Macy, R.B. Pattern Recognition with neural networks in C++. CRC-Press, Inc., Boca Raton, Fl, 1996

44

Popper, K.R. The logic of scientific discovery New York, NY 1959

45

Quinlan, J.R. Induction of decision trees. Machine Learning, 1, 81-106

46

Shen, W-M. et al. An Overview of Database Mining Techniques. http://www.isi.edu/ shen/Tsur.ps

47

Srikant, R. and Agrawal, R. Mining Generalized Association Rules. Proceedings of the 21st VLDB Conference, Zurich, Swizerland, 1995

48

Veelentwurf, L.P.J. Analysis and applications of artificial neural networks. Prentice Hall, Inc., Hertfordshire, UK, 1995

49

Wasserman, P.D. Advanced Methods in Neural Computing. Van Nostrand Reinhold, New York, NY 1993

50

Zwick, M., Shu, H., and Koch, R. Information-Theoretic Mask Analysis of Rainfall Time Series Data. Advances in System Science and Application, 1995, Special Issue I