[Aivazyan, S. A. et al. (1989) Applied Statistics. Classification and Reduction of Dimensionality [in Russian]. FiS, Moscow.]Search in Google Scholar
[Aizerman, M. A., Braverman, E. M. and Rozonoer, L. I. (1970) The Method of Potential Functions in Machine Learning Theory [in Russian]. Nauka, Moscow.]Search in Google Scholar
[Bognar, J. (1974) Indefinite Inner Product Spaces. Springer-Verlag, New York.]Search in Google Scholar
[Braverman, E. M. (1970) Methods of extremal grouping of parameters and problem of apportionment of essential factors [in Russian]. Avtomat. i Telemekh. 1, 123–132.]Search in Google Scholar
[Braverman, E. M. et al. (1971) Diagonalization of the relation matrix and detecting hidden factors [in Russian]. Trans. Inst. of Control Sciences. 1st Issue ‘Problems of increasing of automata possibilities.” ICS, Moscow, 42–79.]Search in Google Scholar
[Braverman, E. M. and Muchnik, I. B. (1983) Structured Methods of Empirical Data Processing [in Russian]. Nauka, Moscow.]Search in Google Scholar
[Diday, E., Bochi, S., Brossier, G. and Celeux, G. (1979) Optimisation en Classification Automatique. 2. Institut national de recherche en informatique et en automatique (INRIA), Le Chesnay (in French).]Search in Google Scholar
[Duda, R. O. and Hart, P. E. (1973) Pattern Classification and Scene Analysis. Wiley, New York.]Search in Google Scholar
[Duda, R. O., Hart, P. E. and Stork, D. G. (2000) Pattern Classification.Wiley, New York.]Search in Google Scholar
[Dvoenko, S. D. (2001) Restoration of spaces in data by the method of non-hierarchical decompositions. Automation and Remote Control. 62, 467– 473. //doi.org/10.1023/A:1002814429456]Search in Google Scholar
[Dvoenko, S. D. (2009a) Clustering and separating of a set of members in terms of mutual distances and similarities. Trans. on MLDM. IBaI Publishing, 2(2), 80–99.]Search in Google Scholar
[Dvoenko, S. D. (2009b) Clustering of a set described by paired distances and closeness between its elements [in Russian]. Sib. J. of Industr. Math. 12 (1), 61–73.]Search in Google Scholar
[Dvoenko, S. D. (2011) On clustering of a set of members by distances and similarities. Proc. of 11th Int. Conf, on Pattern Recognition and Information Processing (PRIP’2011). BSUIR, 104–107.]Search in Google Scholar
[Dvoenko, S. (2014) Meanless k-means as k-meanless clustering with the bipartial approach. Proc. of 12th Int. Conf. on Pattern Recognition and Information Processing (PRIP’2014). UIIP NASB, 50–54.]Search in Google Scholar
[Dvoenko, S. and Owsinski, J. (2019) The permutable k-means for the bipartial criterion. Informatica. 43 (2), 253–262. //doi.org/10.31449/inf.v 43i2.2090]Search in Google Scholar
[Dvoenko, S. D. and Pshenichny, D. O. (2018) On metric correction and conditionality of raw featureless data in machine learning. Pattern Recognit. Image Anal. 28, 595–604. //doi.org/10.1134/S1054661818040089]Search in Google Scholar
[Dvoenko, S. D. and Pshenichny, D. O. (2021) Rank aggregation based on new types of the Kemeny’s median. Pattern Recognit. Image Anal. 31, 185–196. //doi.org/10.1134/S1054661821020061]Search in Google Scholar
[Fisher, R. A. (1936) The use of multiple measurements in taxonomic problems. Ann. Eugenics. 7 (2), 179-188.]Search in Google Scholar
[Friedman, H. P. and Rubin, J. (1967) On some invariant criteria for grouping data. Journal of the American Statistical Association 62 (320), 1159– 1178. //doi.org/10.1080/01621459.1967.10500923]Search in Google Scholar
[Harman, H. H. (1976) Modern Factor Analysis. University of Chicago Press, Chicago.]Search in Google Scholar
[Hartigan, J. A. and Wong, M. A. (1979) Algorithm AS 136: A k-means clustering algorithm. J. Roy. Soc. 28 (1), 100–108. //doi.org/10.2307/2346830]Search in Google Scholar
[Kemeny, J. (1959) Mathematics without numbers. Daedalus, 88(4), 577–591.]Search in Google Scholar
[Litvak, B. G. (1982) Expert Information: Methods of Acquisition and Analysis [in Russian]. Radio i Svyaz, Moscow.]Search in Google Scholar
[Lumel’sky, V. Ya. (1970) Grouping of parameters on the basis of communication matrices [in Russian]. Avtomat. i Telemekh. 1, 133–143.]Search in Google Scholar
[Luce, R. D. (1959) Individual Choice Behavior: A Theoretical Analysis. Wiley, New York.]Search in Google Scholar
[Mercer, J. (1909) Functions of positive and negative type and their connection with the theory of integral equations. Philos. Trans. Roy. Soc., London.]Search in Google Scholar
[Owsiński, J. W. (2020) Data Analysis in Bi-partial Perspective: Clustering and Beyond. SCI 818, Springer. //doi.org/10.1007/978-3-030-13389-4]Search in Google Scholar
[Pekalska, E. AND Duin R. P. W. (2005) The Dissimilarity Representation for Pattern Recognition. Foundations and Applications. World Scientific, Sngapore.]Search in Google Scholar
[Rosenblatt, F. (1962) Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books, Washington.]Search in Google Scholar
[Schlesinger, M. (1965) About spontaneous recognition of patterns [in Russian]. Reading Automations. Kiev, 38–45.]Search in Google Scholar
[Späth, H. (1983) Cluster-Formation und -Analyse: Theorie, FORTRAN-Programme und Beispiele [in German]. R. Oldenbourg Verlag, München– Wien.]Search in Google Scholar
[Torgerson, W. S. (1958) Theory and Methods of Scaling. Wiley, New York.]Search in Google Scholar
[Ward, J. (1963) Hierarchical grouping to optimize an objective function. J. American Statist. Ass. 58 (301), 236–244. //doi.org/10.1080/01621459.1963.10500845]Search in Google Scholar
[Young, G. and Householder, A. S. (1938) Discussion of set of points in terms of their mutual distances. Psychometrica. 3, 19–22. //doi.org/10.1007/BF02287916]Search in Google Scholar
[Zagoruiko, N. G. (1999) Applied Methods of Data and Knowledge Analysis [in Russian]. IM SBRAS, Novosibirsk.]Search in Google Scholar