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9. Ye. Bodyanskiy, V. Kolodyazhniy, A. Stephan, Recursive Fuzzy Clustering Algorithms. Proc. 10th East West Fuzzy Colloquium, Zittau, Germany, 2002, pp. 276 283. 10. V. Barnett, T. Lewis, Outliers in Statistical Data. Chichester-New York-Brisbane-Toronto: John Wiley & Sons, 1978. 11. W. J. J. Rey, Robust Statistical Methods, Lecture Notes in Mathematics, Vol. 690. BerlinHeidelberg-New York: Springer-Verlag, 1978. 12. P. J. Huber, Robust Statistics, New York: John Wiley & Sons, 1981. 13. K. Tsuda, S. Senda, M. Minoh, K. Ikeda, Sequential Fuzzy Cluster Extraction and Its Robustness Against Noise, Systems and Computers in Japan, Vol. 28, pp. 10 17, 1997. 14. F. H ppner, F. Klawonn, Fuzzy Clustering of Sampled Functions, Proc. 19th Int. Conf. of o the North American Fuzzy Information Processing Society (NAFIPS), Atlanta, USA, 2000, pp. 251 255. 15. O. Georgieva, F. Klawonn, A Clustering Algorithm for Identi cation of Single Clusters in Large Data Sets. Proc. 11th East West Fuzzy Colloquium, Zittau, Germany, 2004, pp. 118 125. 16. R. Krishnapuram, J. Keller, A Possibilistic Approach to Clustering, IEEE Trans. on Fuzzy Systems, Vol. 1, pp. 98 110, 1993. 17. R. Krishnapuram, J. Keller, Fuzzy and Possibilistic Clustering Methods for Computer Vision, Neural Fuzzy Systems, Vol. 12, pp. 133 159, 1994. 18. K. Arrow, L. Hurwitz, H. Uzawa, Studies in Nonlinear Programming. Stanford: Stanford University Press, 1958. 19. E. E. Gustafson, W. C. Kessel, Fuzzy Clustering with a Fuzzy Covariance Matrix. Proc. IEEE CDC, San Diego, California, 1979, pp. 761 766. 20. Ye. Gorshkov, I. Kokshenev, Ye. Bodyanskiy, V. Kolodyazhniy, O. Shylo, Robust Recursive Fuzzy Clustering-Based Segmentation of Biological Time Series. Proc. 2006 Int. Symp. on Evolving Fuzzy Systems, Ambleside, UK, 2006, pp. 101 105. 21. P. Angelov, Evolving Rule-Based Models. Heidelberg-New York: Physica-Verlag, 2002.

Bland and Altman s Method A simpler method of estimating agreement between two measures of the same variables was proposed by Bland and Altman (1999) They obtained the limits of agreement by computing the mean difference plus/minus 196 times the standard deviation of the mean differences, assuming that the mean differences are normally distributed in the data Then the standard errors and confidence intervals for the upper and lower limits of agreement were obtained Bland and Altman suggested eyeball examinations of the width of the limits of agreement relative to the confidence intervals of the limits If the width of the limits of agreement is much wider than the confidence intervals of the limits, the two methods are in a disagreement.

Weiner, I B (1996) Some observations on the validity of the Rorschach Inkblot method Psychological Assessment, 8, 206 213 Weiner, J P, & de Lissovoy, G (1993) Razing a Tower of Babel: A taxonomy for managed care and health insurance plans Journal of Health, Politics, Policy, and Law, 18, 75 103 Weinhardt, L S, Carey, M P, Carey, K B, Maisto, S A, & Gordon, C M (2001) The relation of alcohol use to sexual HIV risk behavior among adults with a severe and persistent mental illness Journal of Consulting and Clinical Psychology, 69, 77 84 Weissberg, R P, Kumpfer, K L, & Seligman, M E P (2003) Prevention that works for children and youth: An introduction American Psychologist, 58, 425 432 Weisz, J R, Weiss, B, Alicke, M D, & Klotz, M L (1987).

On the other hand, if the width of the limits of agreement is as narrow as the confidence intervals of the limits, agreement of the two methods is reasonably acceptable Bland and Altman (1999) pointed out that the lack of repeatability (reliability) of each measure might affect the comparison of the two methods of measurement To estimate such differential reliability, they suggested collecting repeated-measure data for each of the two methods of measurement In sports, however, one often has to rely on a brief single measure because of situational constraints In such conditions, the same measurement instrument is provided for the same event twice, in effect, pre- and postcompetition For this reason, the random error is likely to remain the same, and it may not be necessary to obtain multiple measures for each occasion The information about the reliability of the measure, which is common to.

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