Skip to main content
Log in

Book reviews

  • Published:
Journal of Classification Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References

  • ATHERTON, P., Ed. (1965),Classification Research: Proceedings of the 2nd International Study Conference on Classification for Information Retrieval, held at Elsimore, Denmark, 14–18.9, 1964, Copenhagen: Munksgaard.

    Google Scholar 

  • BUCHANAN, B. (1979),Theory of Library Classification, London: Bingley.

    Google Scholar 

  • DAHLBERG, I., and PERREAULT, J. M., Eds. (1982-83),Universal Classification I: Subject Analysis and Ordering Systems, Proceedings of the 4th International Study Conference on Classification Research, Augsburg, 18.6-2.7, 1982, Volumes 1–2, Frankfurt: Indeks.

    Google Scholar 

  • INTERNATIONAL STUDY CONFERENCE ON CLASSIFICATION FOR INFORMATION RETRIEVAL (1957), Dorking, England, 13–17.5.1957, Proceedings, London: Aslib.

  • NEELAMEGHAN, P., Ed. (1979),Ordering Systems for Global Information Networks: Proceedings of the 3rd International Conference on Classification Research, Bombay, 1975, Bangalore.

References

  • FOWLKES, E. B., and MALLOWS, C. L. (1983), “A Method for Comparing Two Hierarchical Clusterings,”Journal of the American Statistical Association, 78, 553–584

    Google Scholar 

  • HUBERT, L. J., and ARABIE, P. (1985), “Comparing Partitions,”Journal of Classification, 2, 193–218.

    Google Scholar 

  • MILLIGAN, G. W. (1994), “Clustering Validation: Results and Implications for Applied Analyses,” inClustering and Classifications, Eds., P. Arabie, L. Hubert, and G. De Soete, River Edge, New Jersey: World Scientific Press, in press.

    Google Scholar 

  • MILLIGAN, G. W., and COOPER, M. C. (1986), “A Study of the Comparability of External Criteria for Hierarchical Cluster Analysis,”Multivariate Behavioral Research, 21, 441–458.

    Google Scholar 

  • MILLIGAN, G. W., and COOPER, M. C. (1987), “Methodology Review: Clustering Methods,”Applied Psychological Measurement, 11, 329–354.

    Google Scholar 

  • MILLIGAN, G. W., and COOPER, M. C. (1988), “A STUDY of Variable Standardization,”Journal of Classification, 5, 181–204.

    Google Scholar 

  • SAS User's Guide Statistics, (1985), Cary, NC: SAS Institute.

  • SHEPARD, R. N., and ARABIE, P. (1979), “Additive Clustering: Representation of Similarities as Combinations of Discrete Overlapping Properties”,Psychological Bulletin, 86, 87–123.

    Google Scholar 

References

  • MCNEILL, D., and FREIBERGER, P. (1993),Fuzzy Logic, New York: Simon and Schuster.

    Google Scholar 

  • SMITHSON, M. (1987),Fuzzy Set Analysis for Behavioral and Social Sciences, New York: Springer-Verlag.

    Google Scholar 

  • ZADEH, L. A. (1965), “Fuzzy Sets,”Information and Control, 8, 338–352.

    Google Scholar 

Reference

  • PANKHURST, R. J. (1978),Biological Identification. The Principles and Practice of Identification Methods in Biology, London:Edward Arnold.

    Google Scholar 

References

  • AITCHISON, J. (1986),The Statistical Analysis of Compositional Data, London: Chapman and Hall.

    Google Scholar 

  • GOWER, J. C. (1966), “Some Distance Properties of Latent Root and Vector Methods Used in Multivariate Analysis,”Biometrika, 53, 325–338.

    Google Scholar 

  • GOWER, J. C. (1977), “The Analysis of Asymmetry and Orthogonality,” inRecent Developments in Statistics, Ed., J. Barra, Amsterdam: North-Holland.

    Google Scholar 

  • KRZANOWSKI, W. J. (1987), “Cross-Validation in Principal Component Analyses,”Biometrics, 43, 575–584.

    Google Scholar 

References

  • BALLARD, D. H., and BROWN, C. M. (1982),Computer Vision, Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • BLAKE, A., and YUILLE, A., Eds. (1992),Active Vision, Cambridge, MA: The MIT Press.

    Google Scholar 

  • GALLANT, S. L. (1993),Neural Network Learning and Expert Systems, Cambridge, MA: The MIT Press.

    Google Scholar 

  • HERTZ, J., KROGH, A., and PALMER, R. G. (1991)Introduction to the Theory of Neural Computation, Reading, MA: Addison-Wesley.

    Google Scholar 

  • RIPLEY, B. D. (1993) “Statistical Aspects of Neural Networks,” inNetworks and Chaos—Statistical and Probabilistic Aspects, Eds., O. E. Barndorff-Nielsen, D. R. Cox, J. L. Jensen, and W. S. Kendall, London: Chapman & Hall.

    Google Scholar 

  • RUSS, J. C. (1992),The Image Processing Handbook, Boca Raton, FL: CRC Press.

    Google Scholar 

References

  • DYKSTRA, R. L. (1983), “An Algorithm for Restricted Least Squares Regression,”Journal of the American Statistical Association, 78, 837–842.

    Google Scholar 

  • GREEN, P. E., and SCHAFFER, C. M. (1991), “Importance Weight Effects on Self-Explicated Preference Models: Some Empirical Findings,”Advances in Consumer Research, 18, 234–251.

    Google Scholar 

  • TUCKER, L. R. (1960), “Intra-individual and Inter-individual Multidimensionality,” inPsychological Scaling: Theory and Applications, Eds., H. Gulliksen and S. Messick, New York: Wiley, 155–167.

    Google Scholar 

References

  • AITCHISON, J. (1986),The Statistical Analysis of Compositional Data, London: Chapman & Hall.

    Google Scholar 

  • BOLLEN, K. A. (1989),Structural Equations with Latent Variables, New York: Wiley.

    Google Scholar 

  • JÖRESKOG, K. G., and SÖRBOM, D. (1988),LISREL 7, A Guide to the Program and Applications, Chicago: SPSS.

    Google Scholar 

  • LANCE, G. N., and WILLIAMS, W. T. (1966), “A Generalized Sorting Strategy for Computer Classifications,”Nature, 212, 218.

    Google Scholar 

  • LITTLE, R. J. A., and RUBIN, D. B. (1987),Statistical Analysis with Missing Data, New York: Wiley.

    Google Scholar 

  • MCLACHLAN, G. J. (1992),Discriminant Analysis and Statistical Pattern Recognition, New York: Wiley.

    Google Scholar 

  • MILLIGAN, G. W., and COOPER, M. C. (1985), “An Examination of Procedures for Determining the Number of Clusters in a Data Set,”Psychometrika, 50, 159–179.

    Google Scholar 

  • MILLIGAN, G. W., and COOPER, M. C. (1988), “A Study of Standardization of Variables in Cluster Analysis,”Journal of Classification, 5, 181–204.

    Google Scholar 

  • OHSUMI, N., and NAKAMURA, N. (1989), “Space-Distorting Properties in Agglomerative Hierarchical Clustering Algorithms and a Simplified Method for Combinatorial Method,” inData Analysis, Learning Symbolic and Numeric Knowledge, Ed., E. Diday, New York: Nova Science Publishers, 103–108.

    Google Scholar 

  • VAN DER HEIJDEN, P. G. M., DE FALGUEROLLES, A., and DE LEEUW, J. (1989), “A Combined Approach to Contingency Table Analysis Using Correspondence Analysis and Log-Linear Analysis (with Discussion),”Applied Statistics, 38, 249–292.

    Google Scholar 

References

  • ARABIE, P. (1991), “Was Euclid and Unnecessarily Sophisticated Psychologist?”Psychometrika, 56, 567–587.

    Google Scholar 

  • EMBERTSON, S. E., “A General Latent Trait Model for Response Processes,”Psychometrika,49, 175–186.

  • GOUGH, H. G. (1987),California Psychological Inventory. Administrator's Guide Palo Alto, CA: Consulting Psychologist Press.

    Google Scholar 

  • HARSHMAN, R. A., and LUNDY, M. E. (1984), “The PARAFAC Model for Three-Way Factor Analysis and Multidimensional Scaling,” inResearch Methods for Multi-mode Data Analysis, Eds., H. G. Law et al., New York: Praeger, 122–215.

    Google Scholar 

  • HAWIK-R (1984), Hamburg-Wechsler Intelligenztest für Kinder-Revision 1983 [Hamburg-Wechsler Test of Intelligence for Children-Revision 1983: HAWIK-R], Edited and revised by U. Tewes (2nd ed.), Bern: Huber.

    Google Scholar 

  • JÖRESKOG, K. G. (1962), “On the Statistical Treatment of Residuals in Factor Analysis,”Psychometrika, 27, 335–345.

    Google Scholar 

  • RASCH, G. (1960),Probabilistic Models for Some Intelligence and Attainment Tests, Copenhagen: Denmarks Paedogogiske Institut.

    Google Scholar 

  • STOCKING, M. L. (1990), “Specifying Optimum Examinees for Item Parameter Estimation in Item Response Theory,”Psychometrika, 55, 461–475.

    Google Scholar 

References

  • BORGATTI, S. P., Ed. (1992), “Special Issue on Blockmodeling,”Social Networks 14.

  • KNOKE, D., and KUKLINSKI, J. H. (1982),Network Analysis, Beverly Hills: Sage.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hand, D.J., Frank, O., Gödert, W. et al. Book reviews. Journal of Classification 11, 251–296 (1994). https://doi.org/10.1007/BF01195682

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01195682

Navigation