ANDERSON, T. (1951), “Estimating Linear Restrictions on Regression Coefficients for Multivariate Normal Distributions,” The Annals of Mathematical Statistics 22
ARABIE, P., and HUBERT, L. (1994), “Cluster Analysis in Marketing Research,” in Handbook of Marketing Research, ed. R. Bagozzi, Oxford: Blackwell, pp. 160–189.
BERKOWITZ, L. (1989), “Frustration-aggression Hypothesis: Examination and Reformulation,” Psychological Bulletin 106
BOCK, H.-H. (1987), “On the interface Between Cluster Analysis, Principal Component Analysis and Multidimensional Scaling,” in Multivariate Statistical Modeling and Data Analysis: Proceedings of the Advanced Symposium on Multivariate Modeling and Data Analysis May 15–16, 1986, eds. H. Bozdogan and A. Gupta, Dordrecht, The Netherlands: Reidel Publishing Company, pp. 17–34.
CARROLL, J. D., and CHATURVEDI, A. (1995), “A General Approach to Clustering and Multidimensional Scaling of Two-way, Three-way or Higher-way Data,” in Geometric Representations of Perceptual Phenomena: Papers in honor of Tarow Indow on his 70th birthday, eds. D. R. Luce, M. D’Zmura, D. Hoffman, G. J. Iverson, and K. A. Romney, Mahwah, New Jersey: Lawrence Erlbaum Associates, pp. 295–318.
CATTELL, R. B. (1966), “TheMeaning and Strategic Use of Factor Analysis,” in Handbook of Multivariate Experimental Psychology, ed. R. B. Cattell, Chicago: Rand McNally, pp. 174–243.
CEULEMANS, E., and KIERS, H. A. L. (2006), “Selecting Among Three-mode Principal Component Models of Different Types and Complexities: A Numerical Convex Hull Based Method,” British Journal of Mathematical and Statistical Psychology 59
CEULEMANS, E., TIMMERMAN, M. E., and KIERS, H. A. L. (2011), “The CHull Procedure for Selecting Among Multilevel Component Solutions,” Chemometrics and Intelligent Laboratory Systems 106
CEULEMANS, E., and VAN MECHELEN, I. (2005), “Hierarchical Classes Models for Three-way Three-mode Binary Data: Interrelations and Model Selection,” Psychometrika 70
CEULEMANS, E., and VAN MECHELEN, I. (2004), “Tucker2 Hierarchical Classes Analysis,” Psychometrika 69
CEULEMANS, E., VAN MECHELEN, I., and LEENEN, I. (2007), “The Local Minima Problem in Hierarchical Classes Analysis: An Evaluation of a Simulated Annealing Algorithm and Various Multistart Procedures,” Psychometrika 72
CEULEMANS, E., VAN MECHELEN, I., and LEENEN, I. (2003), “Tucker3 Hierarchical Classes Analysis,” Psychometrika 68
CHANG, W.-C. (1983), “On Using Principal Components Before Separating a Mixture of Two Multivariate Normal Distributions,” Applied Statistics 32
CHATURVEDI, A., and CARROLL, J. D. (1994), “An Alternating Combinatorial Optimization Approach to Fitting the INDCLUS and Generalized INDCLUS Models,” Journal of Classification 11
COHEN, J., and COHEN, P. (1983), Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (2nd ed.), Hillsdale, NJ: Erlbaum.
DEPRIL, D., VAN MECHELEN, I., and MIRKIN, B. G. (2008), “Algorithms for Additive Clustering of Rectangular Data Tables,” Computational Statistics and Data Analysis 52
DE SOETE, G., and CARROLL, J. D. (1994), “K-means Clustering an a Low-dimensional Euclidean Space,” in New Approaches in Classification and Data Analysis, eds. E. Diday, Y. Lechevallier, M. Schader, P. Bertrand, and B. Burtschy, Berlin, Germany: Springer-Verlag, pp. 212–219.
EVERITT, B. (1977), “Cluster Analysis,” in The Analysis of Survey Data, Vol. 1: Exploring Data Structures, eds. C. A. O’Muircheartaig and C. Payne, London: Wiley, pp. 63–88.
HUBERT, L. J., ARABIE, P., and HESSON-MCINNES, M. (1992), “Multidimensional Scaling in the City-block Metric - A Combinatorial Approach,” Journal of Classification 9
KRZANOWSKI,W. (1979), “Between-groups Comparison of Principal Components,” Journal of the American Statistical Association 74
KUPPENS, P., and VAN MECHELEN, I. (2007), “Determinants of the Anger Appraisals of Threatened Self-esteem, Other-blame, and Frustration,” Cognition and Emotion 21
KUPPENS, P., VANMECHELEN, I., and SMITS, D. J.M. (2003), “The Appraisal Basis of Anger: Specificity, Necessity and Sufficiency of Components,” Emotion 3
LEE, D. D., and SEUNG, S. H. (2001), “Algorithms for Non-negativeMatrix Factorization,” Advances in Neural Information Processing Systems 13, 556–562.
LEE, D. D., and SEUNG, S. H. (1999), “Learning the Parts of Objects by Non-negative Matrix Factorization,” Nature 401
MIRKIN, B. G. (1987), “Method of Principal Cluster Analysis,” Automation and Remote Control 48
ROCCI, R., and VICHI,M. (2005), “Three-mode Component Analysis with Crisp or Fuzzy Partition of Units,” Psychometrika 70
SCHEPERS, J., CEULEMANS, E., and VAN MECHELEN, I. (2008), “Selecting Among Multi-mode Partitioning Models of Different Complexities: A Comparison of Four Model Selection Criteria,” Journal of Classification 25
SHEPARD, R. N., and ARABIE, P. (1979), “Additive Clustering Representations of Similarities as Combinations of Discrete Overlapping Properties,” Psychological Review 86
SPIELBERGER, C. D., JOHNSON, E. H., RUSSELL, S. F., CRANE, J. C., JACOBS, G. A., and WORDEN, T. J. (1985), “The Experience and Expression of Anger: Construction and Validation of an Anger Expression Scale,” in Anger and Hostility in Cardiovascular and Behavioral Disorders, eds. M. A. Chesney and R. H. Rosenman, New York: Hemisphere, pp. 5–30.
STEINLEY, D. (2003), “Local Optima in K
-means Clustering: What You Don’t Know May Hurt You,” Psychological Methods 8
STEINLEY, D., and BRUSCO, M. J. (2007), “Intializing K
-means Batch Clustering: A Critical Evaluation of Several Techniques,” Journal of Classification 24
STOICA, P., and VIBERG, M. (1996), “Maximum Likelihood Parameter and Rank Estimation in Reduced-Rank Multivariate Linear Regressions,” IEEE Transactions on Signal Processing 44, 3096–3078.
TRYON, R. C., and BAILY, D. E. (1970), Cluster Analysis, New York: McGraw-Hill.
VICHI, M., and KIERS, H. A. L. (2001), “FactorialK
-means Analysis for Two-Way Data,” Computational Statistics and Data Analysis 37
VICHI, M., ROCCI, R., and KIERS, H. A. L. (2007), “Simultaneous Component and Clustering Models for Three-Way Data: Within and Between Approaches,” Journal of Classification 24
WILDERJANS, T. F., CEULEMANS, E., and KUPPENS, P. (2012), “Clusterwise HICLAS: A Generic Modeling Strategy to Trace Similarities and Differences in Multi-Block Binary Data,” Behavior Research Methods, 44
WILDERJANS, T. F., CEULEMANS, E., and MEERS, K. (in press), “CHull: A Generic Convex Hull Based Model Selection Method,” Behavior Research Methods.
WILDERJANS, T. F., CEULEMANS, E., and VAN MECHELEN, I. (in press), “The SIMCLAS Model: Simultaneous Analysis of Coupled Binary Data Matrices with Noise Heterogeneity Between and Within Data Blocks,” Psychometrika.
WILDERJANS, T. F., CEULEMANS, E., VAN MECHELEN, I., and DEPRIL, D. (2011), “ADPROCLUS: A Graphical User Interface for Fitting Additive Profile Clustering Models to Object by Variable Data Matrices,” Behavior Research Methods 43