A monte carlo study of thirty internal criterion measures for cluster analysis

• Downton, M., & Brennan, T. Comparing classifications: An evaluation of several coefficients of partition agreement. Paper presented at the meeting of the Classification Society, Boulder, Colorado, June 1980.

• Dudewicz, E. J.IRCCRAND-The Ohio State University random number generator package (Tech. Rep. No. 104). Columbus, Ohio: The Ohio State University, Department of Statistics, 1974.

  Google Scholar 

• Edelbrock, C., & McLaughlin, B. Intraclass correlations as metrics for hierarchical cluster analysis: Parametric comparisons using the mixture model. Paper presented at the meeting of the Classification Society, Gainesville, Florida, April 1979.

• Fowlkes, E. B., & Mallows, C. L.A new measure of similarity between two hierarchical clusterings and its use in studying hierarchical clustering methods. Paper presented at the meeting of the Classification Society, Boulder, Colorado, June 1980.

  Google Scholar 

• Learmonth, G. P., & Lewis, P. A. W.Naval Postgraduate School random number generator package LLRANDOM (Tech. Rep. NPS55LW73061A). Monterey, Calif.: Naval Postgraduate School, Department of Operations Research and Administrative Sciences, 1973.

  Google Scholar 

References

• Arnold, S. J. A test for clusters.Journal of Marketing Research, 1979,16, 545–551.

  Google Scholar 

• Anderberg, M. R.Cluster analysis for applications. New York: Academic Press, 1973.

  Google Scholar 

• Baker, F. B., & Hubert, L. J. Measuring the power of hierarchical cluster analysis.Journal of the American Statistical Association, 1972,70, 31–38.

  Google Scholar 

• Blashfield, R. K. Mixture model tests of cluster analysis: Accuracy of four agglomerative hierarchical methods.Psychological Bulletin, 1976,83, 377–388.

  Google Scholar 

• Cormack, R. M. A review of classification.Journal of the Royal Statistical Society, Series A, 1971,134, 321–367.

  Google Scholar 

• Dudewicz, E. J. Speed and quality of random numbers for simulation.Journal of Quality Technology, 1976,8, 171–178.

  Google Scholar 

• Edelbrock, C. Comparing the accuracy of hierarchical clustering algorithms: The problem of classifying everybody.Multivariate Behavioral Research, 1979,14, 367–384.

  Google Scholar 

• Friedman, H. P., & Rubin, J. On some invariant criteria for grouping data.Journal of the American Statistical Association, 1967,62, 1159–1178.

  Google Scholar 

• Guilford, J. P., & Fruchter, B.Fundamental statistics in Psychology and Education. New York: McGraw-Hill, 1973.

  Google Scholar 

• Hartigan, J. A.Clustering algorithms. New York: Wiley, 1975.

  Google Scholar 

• Hubert, L. J., & Levin, J. R. A general statistical framework for assessing categorical clustering in free recall.Psychological Bulletin, 1976,83, 1072–1080.

  Google Scholar 

• Jardine, N., & Sibson, R.Mathematical taxonomy. New York: Wiley, 1971.

  Google Scholar 

• Johnson, S. C. Hierarchical clustering schemes.Psychometrika, 1967,32, 241–254.

  Google Scholar 

• Lingoes, J. C. & Cooper, T. PEP-I: A FORTRAN IV (G) program for Guttman-Lingoes nonmetric probability clustering.Behavioral Science, 1971,16, 259–261.

  Google Scholar 

• McClain, J. O., & Rao, V. R. CLUSTISZ: A program to test for the quality of clustering of a set of objects.Journal of Marketing Research, 1975,12, 456–460.

  Google Scholar 

• Milligan, G. W. An examination of the effect of six types of error perturbation on fifteen clustering algorithms.Psychometrika, 1980,45, 325–342.

  Google Scholar 

• Milligan, G. W., & Isaac, P. D. The validation of four ultrametric clustering algorithms.Pattern Recognition, 1980,12, 41–50.

  Google Scholar 

• Milligan, G. W., & Mahajan, V. A note on procedures for testing the quality of a clustering of a set of objects.Decision Sciences, 1980,11, 669–677.

  Google Scholar 

• Rand, W. M. Objective criteria for the evaluation of clustering methods.Journal of the American Statistical Association, 1971,66, 846–850.

  Google Scholar 

• Rohlf, F. J. Methods of comparing classifications.Annual Review of Ecology and Systematics, 1974,5, 101–113.

  Google Scholar 

• Rohlf, F. J., & Fisher, D. R. Tests for hierarchical structure in random data sets.Systematic Zoology, 1968,17, 407–412.

  Google Scholar 

• Sneath, P. H. A. Evaluation of clustering methods. In A. J. Cole, (Ed.),Numerical taxonomy. New York: Academic Press, 1969.

  Google Scholar 

• Sneath, P. H. A. Basic program for a significance test for clusters in UPGMA dendrograms obtained from squared euclidean distance.Computer Geosciences, 1979,5, 127–137.(a)

  Google Scholar 

• Sneath, P. H. A. Basic program for a significance test for 2 clusters in euclidean space as measured by their overlap.Computer Geosciences, 1979,5, 143–155.(b)

  Google Scholar 

• Williams, W. T., Clifford, H. T., & Lance, G. N. Group-size dependence: A rationale for choice between numerical classifications.Computer Journal, 1971,14, 157–162.

  Google Scholar 

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