Algorithms for Multidimensional Scaling

Relevant Literature References



Abney, S., Volinsky, C., and Buja, A.

Mining large call graphs, AT&T Labs Technical Memorandum, Florham Park, NJ, 1998.


Akkucuk, U., Carroll, J. D.



Nonlinear Mapping: Approaches Based on Optimizing an Index of Continuity and pplying Classical Metric MDS to Revised Distances

Brusco, M.J.


Morph-based local-search heuristics for large-scale combinatorial data analysis, Journal of Classification, 16 (1999), 163-180.

Brusco, M.J. and Stahl, S.



Using quadratic assignment methods to generate initial permutations for least-squares unidimensional scaling of symmetric proximity matrices, Working Paper, Florida State University, 2000.

Brusco, M.J. and Stahl, S.

A simulated annealing heuristic for unidimensional and multidimensional (city-block) scaling of symmetric proximity matrices, Working Paper, Florida State University, 2000.


Buja, A., Swayne, D.F., Littman, M.L., and Dean, N.

XGvis: Interactive data visualization with multidimensional scaling, J. of Computational and Graphical Statistics, 2002, to appear.


Busing, F.M.T.A., Commandeur, J.J.F., and Heiser, W.J.

PROXSCAL: A multidimensional scaling program for individual differences scaling with constraints. In W. Bandilla and F. Faulbaum (Eds.), Softstat'97: Advances in Statistical Software, Vol 6 . Stuttgart: Lucius and Lucius (1997), 67-74.


Buja, A. and Swayne, D.F.

Visualization methodology for Multidimensional Scaling. Journal of Classification, March 2002. Special invited article.


Carroll, J.D.

Some multidimensional scaling and related procedures devised at Bell Laboratories, with ecological applications, in P. Legendre and L. Legendre (eds.), Developments in Numerical Ecology, NATO ASI Series, Vol. G14, Springer-Verlag, Berlin-Heidelberg, 1987, 65-138.


Carroll, J.D. and Arabie, P.

Multidimensional scaling, in M.H. Birnbaum (ed.), Handbook of Perception and Cognition, Volume 3: Measurement, Judgment and Decision Making, Academic Press, San Diego, CA, 1998, 179-250.


Carroll, J.D., Arabie, P., Chaturvedi, A. D. and Hubert, L. J.

Multidimensional scaling and clustering in marketing: Paul Green’ s role.  In Y. J. Wind, P. E. Green, & R. Gunther (Eds.), Advances in marketing research and modeling.  Norwell, MA: Kluwer Academic Publishers. (in press).


Carroll, J.D. and Chang, J.J.

Analysis of individual differences in multidimensional scaling via an N-way generalization of ``Eckart-Young'' decomposition, Psychometrika, 35 (1970), 283-319. [Reprinted in P. Davies and A. P. M. Coxon (eds.), Key Texts in Multidimensional Scaling, Heinemann, Portsmouth, NH, 1984, 229-252.]


Carroll, J.D. and Chaturvedi, A.

A general approach to clustering and multidimensional scaling of two-way, three-way, or higher-way data, in R. D. Luce, M. D'Zmura, D. D. Hoffman, G. Iverson and A. K. Romney (eds.), Geometric Representations of Perceptual Phenomena, Erlbaum, Mahwah, NJ, 1995, 295-318.


Carroll, J.D. and Corter, J.

A graph-theoretic method for organizing overlapping clusters into trees, multiple trees, or extended trees, Journal of Classification, 12 (1995), 283-313.


Carroll, J.D., De Soete, G., and Pruzansky, S.

A comparison of three rational initialization methods for INDSCAL, in E. Diday (ed.), Data Analysis and Informatics V, North-Holland, Amsterdam, 1988, 131-142.


Carroll, J.G., De Soete, G., and Pruzansky, S.

An evaluation of five algorithms for generating an initial configuration for SINDSCAL, Journal of Classification, 6 (1989), 105--119.


Carroll, J.D. and Pruzansky, S.

The CANDECOMP-CANDELINC family of models and methods for multidimensional data analysis, in H. G. Law, W. Snyder, J. Hattie, and R. P. McDonald (eds.), Research Methods for Multimode Data Analysis, Praeger, New York, 1984, 372-402.


Carroll, J.D. and Pruzansky, S.

Discrete and hybrid models for proximity data, in W. Gaul and M. Schader (eds.) Classification as a Tool of Research, North-Holland, Amsterdam, 1986, 47-59.


Carroll, J.D., Pruzansky, S., and Kruskal, J.B.

CANDELINC: A general approach to multidimensional analysis of many-way arrays with linear constraints on parameters, Psychometrika, 45 (1980), 3-24.


Chandon, J.L. and De Soete, G.

Fitting a least squares ultrametric to dissimilarity data: Approximation versus optimization, in E. Diday, M. Jambu, L. Lebart, J. Pages, and R. Tomassone (eds.), Data Analysis and Informatics III, North-Holland, Amsterdam, 1984, 213-221.


Chaturvedi, A. and Carroll, J.D.

Differentiation due to Quantitative and Qualitative Perceptions of products: Application of a Hybrid Model Incorporating Overlapping Clustering and Multidimensional Scaling Structure, manuscript.


Chen, G.

Metric two-way multidimensional scaling and circular unidimensional scaling: Global optimization by mixed integer programming approaches, Doctoral Dissertation, Rutgers University, 2000.


Corter, J.E

ADDTREE/P: A PASCAL program for fitting additive trees based on Sattath & Tversky's ADDTREE algorithm. Behavior Research Methods and Instrumentation, 14 (1982) 353-354.


Corter, J.E and Tversky A.

Extended similarity trees. Psychometrika, 51 (1986), 429-451.


Corter, J.E

Tree Models of Similarity and Association. University Papers series: Quantitative Applications in the Social Sciences, series no. 07-112). Thousand Oaks CA: Sage, 1996.


Corter, J.E

An efficient metric combinatorial algorithm for fitting additive trees. Multivariate Behavioral Research, 33 (1998), 249-272.


Crippen, G.M. and Havel, T.F.

Stable calculation of coordinates from distance information, Acta Crystallographica, A34 (1978), 282-284.


Critchley, F. and Heiser, W.J.

Hierarchical trees can be perfectly scaled in one dimension. Journal of Classification, 5 (1988), 5-20.


Glunt, W., Hayden, T.L, and Raydan, M.

Molecular conformation from distance matrices, J. Computational Chemistry, 14 (1993), 114-120.


Groenen, P.J.F. and Heiser, W.J.

The tunneling method for global optimization. Psychometrika, 61 (1996), 529-550.


Groenen, P.J.F., Heiser, W.J. and Meulman, J.J.

City-block scaling: Smoothing strategies for avoiding local minima. In I. Balderjahn, R. Mathar and M. Schader (Eds.), Classification, Data Analysis, and Data Highways. Berlin: Springer Verlag (1998), 46-53.


Groenen, P.J.F., Heiser, W.J. and Meulman, J.J.

Global optimization in least squares multidimensional scaling by distance smoothing. Journal of Classification, 16 (1999), 225-254.


Groenen, P.J.F. and Heiser, W.J.

Iterative majorization algorithms in statistical computing. Discussion of: K. Lange, D.R Hunter and I. Yang, Optimization transfer using surrogate objective functions. Journal of Computational and Graphical Statistics, 9 (2000), 44-48.


Groenen, P.J.F., Mathar, R. and Heiser, W.J.

The majorization approach to multidimensional scaling for Minkowski distances. Journal of Classification, 12 (1995), 3-19.


Gurden, S.P., Westerhuis, J.A., Bijlsma, S. and Smilde, A.,

Modeling of spectroscopic batch process data using Grey models to incorporate external information, Journal of Chemometrics, 15 (2001), 101-121.


Hagerty, C.G., Muchnik, I., Kulikowski, C., and Kim, S-H.

Two indices can approximate four hundred and two amino acid properties, Proceedings of the 1999 IEEE International Symposium on Intelligent Control/Intelligent Systems and Semiotics, Cambridge, MA, September 1999, 365-369.


Harshman, R.A.

Foundations of the PARAFAC procedure: models and conditions for an "explanatory" multi-mode factor analysis, UCLA Working Papers In Phonetics, 16 (1970), 1-84.


Harshman, R.A.

Determination and proof of minimum uniqueness conditions for PARAFAC1, UCLA Working Papers In Phonetics, 16 (1972), 1-84.


Havel, T.F.

An evaluation of computational strategies for use in the determination of protein structure from distance constraints obtained by nuclear magnetic resonance, Progress in Biophysics and Molecular Biology, 56 (1991), 43-78.

Heiser, W.J.

Order invariant unfolding analysis under smoothness restrictions, in G. De Soete, H. Feger, and C. Klauer (eds.), New Developments in Psychological Choice Modeling, North-Holland, Amsterdam, 1989, 3-31.


Heiser, W.J. and Lauro, N.

Multidimensional scaling and classification techniques: An introduction. In R. Coppi and S. Bolasco (Eds.), Analysis of Multiway Data Matrices. Amsterdam: North-Holland (1989), 393-394.


Heiser, W.J. and Meulman, J.

Analyzing rectangular tables with joint and constrained multidimensional scaling. Journal of Econometrics, 22 (1983), 139-167.


Heiser, W.J. and Meulman, J.

Constrained multidimensional scaling, including confirmation. Applied Psychological Measurement, 7 (1983), 381-404.


Heiser, W.J. and Meulman, J.

Representation of binary multivariate data by graph models using the Hamming metric. In E. Wegman and S. Azen (Eds.), Computing Science and Statistics, Vol.29(2). Fairfax, VA: Interface Foundation of North America (1997), 517-525.


Heiser, W.J. and Bennani, M.

Triadic distance models for triadic dissimilarity data. Journal of Mathematical Psychology, 41 (1997), 189-206.


Heiser, W.J. and Groenen, P.J.F.

Cluster differences scaling with a within-clusters loss component and a fuzzy successive approximation strategy to avoid local minima. Psychometrika, 62 (1997), 63-83.


Heiser, W.J.

A generalized majorization method for least squares multidimensional scaling of pseudodistances that may be negative. Psychometrika, 55 (1991), 7-27.


Heiser, W.J.

Book review of: A.P.M. Coxon, The User's Guide to Multidimensional Scaling. Journal of Classification, 1 (1984), 271-274.


Heiser, W.J.

Book review of: I. Borg and J. Lingoes, Multidimensional Similarity Structure Analysis. Journal of Classification, 5 (1988), 302-304.


Heiser, W.J.

Book review of: J.C. Gower and D.J. Hand, Biplots. Journal of Classification, 15 (1998), 143-148.


Heiser, W.J.

Book review of: T.F. Cox and M.A.A. Cox, Multidimensional Scaling. Statistics and Computing, 6 (1996), 389-390.


Heiser, W.J.

Book review of: W. Hartmann, Geometrische Modelle zur Analyse Empirischen Daten. Psychometrika, 46 (1981), 111-112.


Heiser, W.J.

Clustering in low-dimensional space. In O. Opitz, B. Lausen and R. Klar (Eds.), Studies in Classification, Data Analysis, and Knowledge Organization. Heidelberg: Springer Verlag (1993), 162-173.


Heiser, W.J.

Convergent computation by iterative majorization: Theory and applications in multidimensional data analysis. In W.J. Krzanowski (Ed), Recent Advances in Descriptive Multivariate Analysis. Oxford: Oxford University Press (1995), 157-189.


Heiser, W.J.

Correspondence analysis with least absolute residuals. Computational Statistics and Data Analysis, 5 (1987), 337-356.


Heiser, W.J.

Correspondence Analysis. In N.J. Smelser and P.B. Baltes (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford: Pergamon (2001), 4, 2820-2824.


Heiser, W.J.

Distances and their approximation. In De Leeuw, J. et al (Eds.) Multidimensional Data Analysis. Leiden; DSWO Press, 1986, 47-52.


Heiser, W.J.

Fitting graphs and trees with multidimensional scaling methods. In C. Hayashi, N. Ohsumi, K. Yajima, Y Tanaka, H.-H. Bock and Y. Baba (Eds.), Data Science, Classification, and Related Methods. Tokyo: Springer Verlag (1998), 52-62.


Heiser, W.J.

Joint ordination of species and sites: the unfolding technique. In P. Legendre et al. (Eds.), Developments in Numerical Ecology. New York: Springer (1987), 189-221.


Heiser, W.J.

Multidimensional scaling with least absolute residuals. In H.- H. Bock (Ed.) Classification and Related Methods of Data Analysis, Proceedings of the First Conference of the International Federation of Classification Societies (IFCS '87). Amsterdam: North-Holland (1988), 455-462.


Heiser, W.J.

Nested solutions in least squares multidimensional scaling. Statistics in Transition, Journal of the Polish Statistical Association, 2 (1995), 237-249.


Heiser, W.J.

PROXSCAL: multidimensional scaling of proximities. In A. di Ciaccio and G. Bove (Eds.) Proceedings of MULTIWAY'88, Sofware Guide. Rome: Universitŕ di Roma "La Sapienza" (1988), p. 77-81.


Heiser, W.J.

Selecting a stimulus set with prescribed structure from empirical confusion frequencies. British Journal of Mathematical and Statistical Psychology, 41 (1988), 37-51.


Heiser, W.J.

The city-block model for three-way multidimensional scaling. In R. Coppi and S. Bolasco (Eds.), Analysis of Multiway Data Matrices. Amsterdam: North-Holland (1989), 395-404.


Hubert, L.J. and Arabie, P.

Unidimensional scaling and combinatorial optimization, in J. de Leeuw, W. Heiser, J. Meulman, and F. Critchley (eds.), Multidimensional Data Analysis, DSWO Press, Leiden 1986, 181-196.


Hubert, L.J. and Arabie, P

Iterative projection strategies for the least-squares fitting of tree structures to proximity data, British J. Math. Statist. Psych., 48 (1995), 281-317.


Hubert, L.J. and Arabie, P.

The approximation of two-mode proximity matrices by sums of order-constrained matrices, Psychometrika, 60 (1995), 573--605.


Hubert, L.J., Arabie P., and Hesson-Mcinnis, M.

Multidimensional scaling in the city-block metric: A combinatorial approach, Journal of Classification, 9 (1992), 211--236.


Hubert, L.J., Arabie, P., and Meulman, J.

Linear and circular unidimensional scaling for symmetric proximity matrices, British J. Math. Statist. Psych., 50, 253-284.


Hubert, L.J., Meulman, J.J. and Heiser, W.J.

Two Purposes for Matrix Factorization: A Historical Appraisal. SIAM Review, 42 (2000), 68-82.


Kiers, H.A.L.

Three-way SIMPLIMAX for oblique rotation of the three-mode factor analysis core to simple structure, Computational Statistics & Data Analysis, 28 (1998), 307-324.


Kiers, H.A.L., Ten Berge, J.M.F., and Rocci, R.

Uniqueness of three-mode factor models with sparse cores: the 3´ 3´ 3 case, Psychometrika, 62 (1997), 349-374.


Kroonenberg, P.M. and de Leeuw, J.

Principal component analysis of three-mode data by means of alternating least-squares, Psychometrika, 45 (1980), 69-97.


Kroonenberg, P.M. and Heiser, W.J.

Parallel factor analysis with constraints on the configurations: An overview. In C. Hayashi, N. Ohsumi, K. Yajima, Y Tanaka, H.-H. Bock and Y. Baba (Eds.), Data Science, Classification, and Related Methods. Tokyo: Springer Verlag (1998), 587-597.


Kruskal, J.B.

Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis, Psychometrika, 29 (1964), 1-27.


Kruskal, J.B.

Nonmetric multidimensional scaling: A numerical method, Psychometrika, 29 (1964), 115-129.


Kruskal, J.B. and Hart, R.E.

A geometric interpretation of diagnostic data from a digital machine: Based on a study of the Morris, Illinois Electronic Central Office, Bell Sys. Tech. J., 45 (1966), 1299-1338.


Kruskal, J.B.

Three-way arrays: rank and uniqueness of trilinear decompositions with applications to arithmetic complexity and statistics. Lin. Alg. & Appl., 18 (1977), 95-138.


Kruskal, J.B.

Statement of some current results about three-way arrays, (1983), Unpublished.


Kruskal, J.B.

Rank, decomposition and uniqueness for 3-way and n-way arrays, in R. Coppi and S. Bolasco (Eds.), Multiway Data Analysis, Amsterdam: North-Holland, (1989), 7-18.


De Leeuw, J. and Heiser, W. J.

Convergence of correction-matrix algorithms for multidimensional scaling, in J. C. Lingoes (ed.), Geometric Representations of Relational Data: Readings in multidimensional scaling, Mathesis Press, Ann Arbor, MI, 1977, 735-752.


De Leeuw, J. and Heiser, W. J.

Multidimensional scaling with restrictions on the configuration. In P.R. Krishnaiah (ed.), Multivariate Analysis, Vol. V, Amsterdam: North-Holland, 1980, 501-522.


De Leeuw, J. and Heiser, W. J.

Theory of multidimensional scaling. In L. Kanal and P.R. Krishnaiah (Eds.), Handbook of Statistics, Volume II. Amsterdam: North-Holland, 1982, 285-317.


Legendre, P. and Legendre, L.

Numerical Ecology, Elsevier, Amsterdam, 1998.


Littman, M., Swayne, D.F., Dean, N., and Buja, A.

Visualizing the embedding of objects in Euclidean space, Computing Science and Statistics: Proc. of the 24th Symp. on the Interface, Interface Foundation of North America, Fairfax Station, VA, 1992, 208-217.


Luczkovich, J.J., Borgatti, S.P., Johnson, J.C., and Everett, M.G.

Defining and measuring trophic role similarity in food webs using regular coloration, in preparation.


Malone, S.W. and Trosset, M.W.

A Study of the Stationary Configurations of the SStress Criterion for Metric Multidimensional Scaling, Technical Report 00-06, Department of Computational & Applied Mathematics, Rice University, (available at


Malone, S.W. and Trosset, M.W.

Optimal Dilations for Metric Multidimensional Scaling, 2000 Proceedings of the Statistical Computing Section, American Statistical Association, (available at


Meulman, J. and Heiser, W.J.

Second order regression and distance analysis. In W. Gaul and M. Schader (Eds.), Data Analysis, Decision Support and Expert Knowledge Representation. Berlin: Springer Verlag (1988), 368-380.


Meulman, J. and Heiser, W.J.

Constrained multidimensional scaling: more directions than dimensions. In T. Havránek et al (Eds.), COMPSTAT 1984. Wien: Physica Verlag, 1984, 137-142.


Meulman, J. and Heiser, W.J.

Non-linear biplots for non-linear mappings. In O. Opitz, B. Lausen and R. Klar (Eds.), Studies in Classification, Data Analysis, and Knowledge Organization. Heidelberg: Springer Verlag (1993), 201-213.


Meulman, J.J., L.J. Hubert, and Heiser, W.J.

The data theory scaling system. In A. Rizzi, M. Vichi and H.-H. Bock (Eds.), Advances in Data Science and Classification. Berlin: Springer Verlag (1998), 489-496.


Morrison, A. and Chalmers, M.

Improving Hybrid MDS with Pivot-Based Searching.  Proceedings Infovis 2003, IEEE Computer Society (to appear).


Rocci, R. and Ten Berge, J.M.F.,

Transforming three-way arrays to maximal simplicity, Psychometrika, 67 (1989), in press.


De Rooij, M.

Three-way distance models and three-way association. J. of  Classification, 19 (2002), 161-178.


De Rooij, M.

Studying triadic distance models under a likelihood function. In Nishisato, S., Baba, Y. Bozdogan, H., and Kanefuji, K. (Eds). Measurement and Multivariate Analysis. pp 69-76,   Tokyo: Springer-Verlag, 2002.


De Rooij, M.

Distance-Association models for the analysis of repeated transition frequency tables. Statistica Neerlandica, 55 (2001), 156-180.


De Rooij, M. and Heiser, W.J.

Triadic distance models for the analysis of asymmetric three-way proximity data. British Journal of Mathematical and Statistical Psychology, 53 (2000), 99-119.


Sidiropoulos, N.D. and Bro, R.

On the uniqueness of multilinear decomposition of n-way arrays, J. of Chemometrics, 14 (2000), 229-239.


Shepard, R.N.

The analysis of proximities: Multidimensional scaling with an unknown distance function. I, Psychometrika, 27 (1962), 125-140.


Shepard, R.N.

The analysis of proximities: Multidimensional scaling with an unknown distance function. II, Psychometrika, 27 (1962), 219-246.


Simantiraki, E.

Unidimensional Scaling: A linear programming approach minimizing absolute deviations, Journal of Classification, 13 (1996), 19-25.


De Soete, G. and Heiser, W.J.

A latent class unfolding model for analyzing single stimulus preference ratings. Psychometrika, 58 (1993), 545-565.


Takane, Y., Hwang, H., Oshima-Takane, Y.

A Kernel Method for Multiple-Set Canonical Correlation Analysis, to appear in IEEE Transactions on Neural Networks.


Ten Berge, J. M.F.

Simplicity transformations and typical rank of three-way arrays, with applications to Tucker-3 analysis, CANDECOMP/PARAFAC, and INDSCAL, manuscript.


Ten Berge, J.M.F.

The typical rank of tall three-way arrays, Psychometrika, 65 (2000), 525-532.


Ten Berge, J.M.F. and Kiers, H.A.L.

Simplicity of core arrays in three-way principal component analysis and the typical rank of p´ q´ 2 arrays. Lin. Alg. & Appl., 294 (1999), 169-179.


Ten Berge, J.M.F., Kiers, H.A.L., Murakami, T., and van Der Heijden, R.

Transforming three-way arrays to multiple orthonormality, Journal of Chemometrics 14 (2000), 275-284.


Ten Berge, J.M.F. and Sidiropoulos, N. D.

On uniqueness in CANDECOMP/PARAFAC, Psychometrika, 67 (2002), in press.


Ten Berge, J.M.F., Sidiropoulos, N. D., and Rocci, R.

Typical rank and INDSCAL dimensionality for symmetric arrays of order I´ 2´ 2 or I´ 3´ 3, (2001), submitted for publication.


Ten Berge, J.M.F. and Smilde, A.K.

Non-triviality and identification of a constrained Tucker-3 analysis, (2001), submitted for publication.


Tijssen, R.J.W., Van Raan, A.F.J., Heiser, W.J., and Wachmann, L.

Integrating multiple sources of information in literature-based maps of science. Journal of Information Science, 16 (1990), 217-227.


Trosset, M.W. and Mathar, R.

On the Existence of Nonglobal Minimizers of the STRESS Criterion for Metric Multidimensional Scaling, Proceedings of the Statistical Computing Section, American Statistical Association, 1997. (available at


Trosset, M.W. and Tarazaga, P.

An Approximate Solution to the Metric SSTRESS Problem in Multidimensional Scaling, Computing Science and Statistics, 30:292-295, 1998, (available at


Trosset, M.W.

Better Initial Configurations for Metric Multidimensional Scaling, (available at


Trosset, M.W.

Applications of Multidimensional Scaling to Molecular Conformation, Computing Science and Statistics, 29:148-152, 1998, (available at


Trosset, M.W.

Computing Distances Between Convex Sets and Subsets of the Positive Semidefinite Matrices, (the latest version is Extensions of Classical Multidimensional Scaling: Computational Theory), Technical Report 97-3, Department of Computational & Applied Mathematics, Rice University, (available at


Trosset, M.W.

Formulations of Multidimensional Scaling for Cluster Analysis and Classification, 1999 Proceedings of the Statistical Computing Section, American Statistical Association, (available at


Tucker, L. R.

Some mathematical notes on three-mode factor analysis, Psychometrika, 31 (1966), 279-311.


Van Buuren, S. and Heiser, W.J.

Clustering n objects into k groups under optimal scaling of variables. Psychometrika, 54 (1989), 699-706.


Van den Berg, G.M., Heiser, W.J. and Commandeur J.J.F.

Comparing repertory grids to integrate knowledge from multiple statisticians. Knowledge Acquisition, 4 (1992), 259-278.


Van der Lans, I.A. and Heiser, W.J.

Constrained part-worth estimation in conjoint analysis using the self-explicated utility model. International Journal of Research in Marketing, 9 (1992), 325-344.


Verboon, P. and Heiser, W.J.

Resistant orthogonal Procrustes analysis. Journal of Classification, 9 (1992), 237-256.


Verboon, P. and Heiser, W.J.

Robust principal components analysis. Computational Statistics and Data Analysis, 18 (1994), 457-467.


Vonk, R. and Heiser, W.J.

Implicit personality theory and social judgment: effects of familiarity with a target person. Multivariate Behavioral Research, 26 (1991), 69-81.


Watts, D.

Small Worlds: The Dynamics of Networks between Order and Randomness, Princeton University Press, Princeton, NJ, 1999.


Wong, R. S-K.

Multidimensional association models. A multilinear approach, Sociological Methods & Research, 30 (2001), 197-240.


Zielman, B. and Heiser, W.J.

Analysis of asymmetry by a slide vector. Psychometrika, 58 (1993), 101-114.


Zielman, B. and Heiser, W.J.

Models for asymmetric proximities. British Journal of Mathematical and Statistical Psychology, 49 (1996), 127-146.





This material is based upon work supported by the National Science Foundation under Grant No. 0100921

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