An overview of reconstructability analysis

Kybernetes

Volumn 33, Issue , 2004, Pages 877-905

  Zwick, Martin ^a  

^a PORTLAND STATE UNIVERSITY   (United States)

Author keywords

Cybernetics; Information theory; Modelling; Systems theory

Indexed keywords

CYBERNETICS; DATA MINING; KNOWLEDGE REPRESENTATION; LEARNING SYSTEMS; MODELS; PROBABILITY DISTRIBUTIONS;

LOGLINEAR MODEL; MULTIVARIATE MODELING; RECONSTRUCTABILITY ANALYSIS; SET-THEORETIC MODELS; STYSTEMS THEORY; THEORETIC MODEL;

INFORMATION THEORY;

EID: 1342294716     PISSN: 0368492X     EISSN: None     Source Type: Journal    
DOI: 10.1108/03684920410533958     Document Type: Article

References (74)

1. Al-Rabadi, A. and Zwick, M. (2004), “Modified reconstructability analysis for many-valued functions and relation”, Kybernetes, Vol. 33 No. 5-6, pp. 906-920.
2. Al-Rabadi, A., Zwick, M. and Perkowski, M. (2004), “A comparison of modified reconstructability analysis and Ashenhurst-Curtis decomposition of Boolean functions”, Kybernetes, Vol. 33 No. 5/6 pp. 933-947.
3. Anderson, D.R. (1996), “The identification problem of reconstructability analysis: a general method for estimation and optimal resolution of local inconsistency”, Systems Science PhD dissertation, Portland State University, Portland, OR.
4. Ashby, W.R. (1964), “Constraint analysis of many-dimensional relations”, General Systems Yearbook, Vol. 9, pp. 99-105.
5. Bishop, Y., Feinberg, S. and Holland, P. (1978), Discrete Multivariate Analysis, MIT Press, Cambridge, MA.
6. Cellier, F. and Yandell, D. (1987), “SAPS-II: a new implementation of the systems approach problem solver”, Int. J. General Systems, Vol. 13 No. 4, pp. 307-322.
7. Cellier, F., Nebot, A., Mugica, F. and de Albornoz, A. (1995), “Combined qualitative-quantitative simulation methods of continuous-time process using fuzzy inductive reasoning techniques”, Int. J. General Systems, Vol. 24 No. 1/2, pp. 95-116.
8. Chambless, B. and Scarborough, D. (2001), “Information-theoretic feature selection for a neural behavioral model”, Int. Joint Conference on Neural Nets, 14-19 July, Washington, DC.
9. Chen, Z. (1994), “Qualitative reasoning for systems reconstruction using Lebesgue discretization”, Int. J. Systems Sci., Vol. 25 No. 12, pp. 2329-2337.
10. Chen, Z. (1997), “K-Systems theory for goal-driven data mining”, Advances in Systems and Applications, Special Issue 1, pp. 40-43.
11. Conant, R.C. (1981), “Set-theoretic structure modeling”, Int. J. General Systems, Vol. 7, pp. 93-107.
12. Conant, R.C. (1988a), “Extended dependency analysis of large systems. Part I: dynamic analysis”, Int. J. General Systems, Vol. 14, pp. 97-123.
13. Conant, R.C. (1988b), “Extended dependency analysis of large systems. Part II: static analysis”, Int. J. General Systems, Vol. 14, pp. 125-141.
14. Davis, J.A. (1985), The Logic of Causal Order, Qantitative Application in the Social Sciences #55, Sage, Beverly Hills, CA.
15. Dobransky, M. and Wierman, M. (1995), “Genetic algorithms: a search technique applied to behavior analysis”, Int. J. General Systems, Vol. 24 Nos 1/2, pp. 125-136.
16. Elias, D. (1988), “The general systems problem solver: a framework for integrating systems methodologies”, PhD dissertation, Department of Systems Science, SUNY-Binghamton.
17. Feldman, D. and Crutchfield, J. (1998), “Discovering noncritical organization: statistical mechanical, information theoretic, and computational views of patterns in one-dimensional spin systems”, Santa Fe Institute Working Paper, 98-04-026.
18. Files, C. and Perkowski, M. (1998), “Multi-valued functional decomposition as a machine learning method”, Proc. ISMVL’98, May 1998.
19. Good, I. (1963), “Maximum entropy for hypothesis formulation especially for multidimensional contingency tables”, Annals of Mathematical Statistics, Vol. 34, pp. 911-934.
20. Grygiel, S. (2000), “Decomposition of relations as a new approach to constructive induction in machine learning and data mining”, Electrical Engineering, PhD dissertation, Portland State University, Portland, OR.
21. Grygiel, S., Zwick, M. and Perkowski, M. (2004), “Multi-level decomposition of relations”, Kybernetes, Vol. 33 Nos. 5/6, pp. 948-961.
22. Hagenaars, J.A. (1993), Loglinear Models with Latent Variables, Quantitative Application in the Social Sciences #94, Sage, Beverly Hills, CA.
23. Hosseini, J., (1987), “Segment congruence analysis: an information theoretic approach”, Systems Sciences PhD dissertation, Portland State University, Portland, OR.
24. Hosseini, J., Harmon, R. and Zwick, M. (1986), “Segment congruence analysis via information theory”, Proceedings, International Society for General Systems Research, May 1986, pp. G62-G77.
25. Hosseini, J., Harmon, R. and Zwick, M. (1991), “An information theoretic framework for exploratory segmentation research”, Decision Sciences, Vol. 22, pp. 663-677.
26. Johnson, M. and Zwick, M. (2000), “State-based reconstructability modeling for decision analysis”, in Ed. Allen, J.K. and Ed. Wilby, J.M. (Eds), Proceedings of The World Congress of the Systems Sciences and ISSS 2000, International Society for the Systems Sciences, Toronto, Canada.
27. Jones, B. (1985a), “Reconstructability analysis for general function”, Int. J. General Systems, Vol. 11, pp. 133-142.
28. Jones, B. (1985b), “Reconstructability considerations with arbitrary data”, Int. J. General Systems, Vol. 11, pp. 143-151.
29. Jones, B. (1986), “K-Systems versus classical multivariate systems”, Int. J. General Systems, Vol. 12, pp. 1-6.
30. Jones, B. (1989), “A program for reconstructability analysis”, Int. J. General Systems, Vol. 15, pp. 199-205.
31. Kim, J. and Muller, C. (1978), Factor Analysis: Statistical Methods and Practical Issues, Quantitative Applications in the Social Sciences #14, Sage, Beverly Hills, CA.
32. Klir, G. (1976), “Identification of generative structure in empirical data”, Int. J. General Systems, Vol. 3 No. 2, pp. 89-104.
33. Klir, G. (1985), The Architecture of Systems Problem Solving, Plenum Press, New York, NY.
34. Klir, G. (1986), “Reconstructability analysis: an offspring of Ashby's constraint theory”, Systems Research, Vol. 3 No. 4, pp. 267-271.
35. Klir, G. (Ed.) (1996). Int. J. General Systems, Special Issue on GSPS, 24 (1-2) (includes an RA bibliography).
36. Klir, G. and Wierman, M.J. (1998), Uncertainty-Based Information: Variables of Generalized Information Theory, Physica-Verlag, New York, NY.
37. Knoke, D. and Burke, P.J. (1980), Log-Linear Models, Quantitative Applications in the Social Sciences Monograph #20, Sage, Beverly Hills, CA.
38. Krippendorff, K. (1981), “An algorithm for identifying structural models of multivariate data”, Int. J. General Systems, Vol. 7 No. 1, pp. 63-79.
39. Krippendorff, K. (1986), Information Theory: Structural Models for Qualitative Data, Quantitative Application in the Social Sciences #62, Sage, Beverly Hills, CA.
40. Ku, H. and Kullbacks, S. (1968), “Interaction in multidimensional contingency tables: an information theoretic approach”, Journal of Research of the National Bureau of Standards – Mathematical Sciences, Vol. 728 No. 3, pp. 159-199.
41. Kullback, S. (1959), Information Theory and Statistics, Wiley, NewYork, NY.
42. Lendaris, G. (1995), “Prestructuring ANNs via a priori knowledge”, Proceedings of SPIE International Conference, SPIE Press.
43. Lendaris, G. and Mathia, K. (1994), “Using a priori knowledge to prestructure ANNs”, Australian Journal of Intelligent Information Systems, Vol. 1 No. 1.
44. Lendaris, G., Rest, A. and Misley, T. (1997), “Improving ANN generalization using a priori knowledge to pre-structure ANNs”, Proceedings of International Conference on Neural Networks '97 (ICNN’97), July 1997, IEEE press, New York, NY.
45. Lendaris, G., Shannon, T. and Zwick, M. (1999), “Prestructuring neural networks for pattern recognition using extended dependency analysis (invited paper)”, Proceedings of Applications and Science of Computational Intelligence II Aero Sense ’99, SPIE, Orlando, FL.
46. Lendaris, G., Zwick, M. and Mathia, K. (1993), “On matching ANN structure to problem domain structure”, Proceedings of the World Congress on Neural Nets ’93, Portland, OR.
47. Li, W. and Packard, N. (1990), “The structure of the elementary cellular automata rule space”, Complex Systems, Vol. 4, pp. 281-297.
48. Long, J.S. (1983), Covariance structure Models: An Introduction to LISREL, Quantitative Application in the Social Sciences #34, Sage, Beverly Hills, CA.
49. Mariano, M. (1984), “Towards resolving inconsistency among behavior systems”, in Ed. Smith, W. (Ed.), Systems Methodologies and Isomorphies, Proceedings – Society for General Systems Research International Conference 1984 Vol. 1, Intersystems Publication, Lewiston, NY, pp. 225-229.
50. Mariano, M. (1987), “Aspects of inconsistency in reconstruction analysis”, PhD dissertation, Department of Systems Science, SUNY-Binghamton.
51. McCutcheon, A.L. (1987), Latent Class Analysis, Quantitative Applications in the Social Sciences #64, Sage, Beverly Hills, CA.
52. Miller, G. and Madow, W. (1954), On the Maximum Likelihood Estimate of the Shannon-Wiener Measure of Information, Air Force Cambridge Research Center, Washington, DC.
53. Mishchenko, A. (2001), “An Introduction to Zero-suppressed binary decision diagrams”, Technical Report June 2001, PSU, available at www.ee.pdx.edu/∼/alanmi/research/dd/zddtut.pdf.
54. Perkowski, M., Marek-Sadowska, M., Jozwiak, L., Luba, T., Grygiel, S., Nowicka, M., Malvi, R., Wang, Z. and Zhang, J. (1997), “Decomposition of many-valued relations”, Proc. ISMVL ’97, May 1997, pp. 13-18.
55. Pittarelli (1990), “Reconstructability analysis using probability intervals”, Int. J. General Systems, Vol. 16, pp. 215-233.
56. Shaffer, G. (1988), “K-systems analysis for determining the factors influencing benthic microfloral productivity in a Louisiana estuary”, USA Mar. Ecol. Prog. Ser., Vol. 43, pp. 43-54.
57. Shaffer, G. and Cahoon, P. (1987), “Extracting information from ecological data containing high spatial and temporal variability: benthic microfloral production”, Int. J. General Systems, Vol. 13, pp. 107-123.
58. Shannon, T. and Zwick, M. (2004), “Directed extended dependency analysis for data mining”, Kybernetes, Vol. 33 Nos 5/6, pp. 973-983.
59. Uyttenhove, H.J.J. (1984), “SAPS – a software systems for inductive modelling”, in Ed. Oren, T.I., et al. (Eds), Simulation and Model-Based Methodologies: An Integrative View, NATO ASI Series, F10, Springer-Verlag, Berlin, Heidelberg, pp. 427-449.
60. Vermunt, J. (1997), Lem: A General Program for the Analysis of Categorical Data, (Program manual), Tilburg University.
61. Walker, C.C. and Ashby, W.R. (1966), “On temporal characteristics of behavior in certain complex systems”, Kybernetes, Vol. 3 No. 2, pp. 100-108.
62. Willett, K. and Zwick, M. (2004), “A software architecture for reconstructability analysis”, Kybernetes, Vol. 33 Nos 5/6, pp. 997-1008.
63. Zadeh, L.A. (1965), “Fuzzy sets”, Information and Control, Vol. 8 No. 3, pp. 338-353.
64. Zwick, M. (1996), “Control uniqueness in reconstructability analysis”, Int. J. General Systems, Vol. 24 Nos. 1-2, pp. 151-162.
65. Zwick, M. (2001), “Wholes and parts in general systems methodology”, in Ed. Wanger, G. (Ed.), The Character Concept in Evolutionary Biology, Academic Press, New York, NY.
66. Zwick, M. (2004a), “Discrete multivariate modeling”, available at: www.sysc.pdx.edu/res_struct.html.
67. Zwick, M. (2004b), “Reconstructability analysis with Fourier transforms”, Kybernetes, Vol. 33 No. 5/6, pp. 1026-1040.
68. Zwick, M. and Johnson, M. (2004), “State-based reconstructability analysis”, Kybernetes, Vol. 33 No. 5/6, pp. 1041-1052.
69. Zwick, M. and Mishchenko, A. (2004), “Binary decision diagrams and reconstructability analysis in crisp possibilistic systems”, (in preparation).
70. Zwick, M. and Shervais, S. (2004), “Reconstructability analysis detection of optimal gene order in genetic algorithms”, Kybernetes, Vol. 33 No. 5/6, pp. 1053-1062.
71. Zwick, M. and Shu, H. (1997), “Set-theoretic reconstructability of elementary cellular automata”, Advances in Systems Science and Applications, Special Issue 1, pp. 31-36.
72. Zwick, M. and Shu, H. (2004), “Reconstructability and dynamics of elementary cellular automata”, (in preparation).
73. Zwick, M. and Zeitler, E. (1973), “Image reconstruction from projections”, Optik, Vol. 38, pp. 550-565.
74. Zwick, M., Shu, H. and Koch, R. (1996), “Information-theoretic reconstructability of rainfall time-series data”, Advances in Systems Science and Application, Special Issue I, pp. 154-159.