Information Technology Reference
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We considered cluster and discriminant analysis as well as regression modeling
with a medical data. We also sketched how fuzzy switching regression analysis,
analysis of covariance and sophisticated cluster analysis could be carried out. We
aimed to justify that fuzzy models are often more usable and conceivable as well as
simpler than the corresponding traditional models. Our results were in accordance
with these aims. At a more general level, we aimed to show that we may acquire
more thorough information on our data and thus enhance the quality of the quantita-
tive medical research. Good medical research, in turn, plays an essential role when
we work for the benefit of humanity.
References
1. Bandemer, H., Näther, W.: Fuzzy Data Analysis. Kluwer, Dordrecht (1992)
2. Bezdek, J.C., Pal, S.K.: Fuzzy Models for Pattern Recognition. IEEE Press, New York
(1992)
3. Celikyilmaz, A., Burhan Türksen, I.: Enhanced Fuzzy System Models with Improved
Fuzzy Clustering Algorithm. IEEE Transactions on Fuzzy Systems 16(3), 779-794
(2008)
4. Chiu, S.L.: Fuzzy Model Identification Based on Cluster Estimation. Journal of Intelli-
gent and Fuzzy Systems 2, 267-278 (1994)
5. Grzegorzewski, P., Hryniewicz, O., Gil, M.-Á.: Soft Methods in Probability, Statistics
and Data Analysis. Physica Verlag, Heidelberg (2002)
6. Guilford, J.P., Fruchter, B.:
Fundamental Statistics in Psychology and Education.
McGraw-Hill, London (1978)
7. Hathaway, R.J., Bezdek, J.C.: Switching Regression Models and Fuzzy Clustering. IEEE
Transactions on Fuzzy Systems 1(3), 195-204 (1993)
8. Hruschka, E.R., Cambello, R.J.G.B., Freitas, A.A., de Carvalho, A.C.P.L.F.: A Survey
of Evolutionary Algorithms for Clustering. IEEE Transactions on Systems, Man and
Cybernetics, Part C 39(2), 133-155 (2009)
9. Kacprzyk, J., Fedrizzi, M.:
Fuzzy Regression Analysis. Physica Verlag, Heidelberg
(1992)
10. Karhunen, V., Rasi, I., Kanniainen, A.: IBM SPSS Statistics, Menetelmiä (methods).
University of Oulu, Oulu (2010)
11. Niskanen, V.A.: A Meta-level Approach to Approximate Probability. In: Setchi, R.,
Jordanov, I., Howlett, R.J., Jain, L.C. (eds.) KES 2010, Part IV. LNCS, vol. 6279, pp.
116-123. Springer, Heidelberg (2010)
12. Niskanen, V.A.: Application of Approximate Reasoning to Hypothesis Verification.
Journal of Intelligent & Fuzzy Systems 21(5), 331-339 (2010)
13. Niskanen, V.A.: Application of Zadeh's Impossibility Principle to Approximate Expla-
nation. In: Proceedings of the IFSA 2009 Conference, Lisbon, pp. 352-360 (2009)
14. Niskanen, V.A.: Fuzzy Systems and Scientific Method - Meta-level Reflections and
Prospects. In: Seising, R. (ed.) Views on Fuzzy Sets and Systems from Different Per-
spectives. STUDFUZZ, vol. 243, pp. 37-65. Springer, Heidelberg (2009)
15. Niskanen, V.A.: Soft Computing Methods in Human Sciences. STUDFUZZ, vol. 134.
Springer, Berlin (2004)
