This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Arabie, P., Hubert, L., & Schleutermann, S. (1990). Blockmodels from the bond energy algorithm. Social Networks, 12, 99-126.ArabieP.HubertL.SchleutermannS.(1990)Blockmodels from the bond energy algorithm.129912610.1016/0378-8733(90)90001-PSearch in Google Scholar
Baier, D., Gaul, W., & Schader, M. (1997). Two-mode overlapping clustering with applications in simultaneous benefit segmentation and market structuring. In Klar R. & Opitz O. (Eds), Classification and knowledge organization (pp. 557-566), Heidelberg: Springer.BaierD.GaulW.SchaderM.(1997)Two-mode overlapping clustering with applications in simultaneous benefit segmentation and market structuring.InKlarR.OpitzO.(Eds)(pp.557566),HeidelbergSpringer10.1007/978-3-642-59051-1_58Search in Google Scholar
Brusco, M. (2011). Analysis of two-mode network data using nonnegative matrix factorization. Social Networks, 33, 201-210.BruscoM.(2011)Analysis of two-mode network data using nonnegative matrix factorization.3320121010.1016/j.socnet.2011.05.001Search in Google Scholar
Brusco, M., & Doreian, P. (2015a). A real-coded genetic algorithm for two-mode KL-means partitioning with application to homogeneity blockmodeling. Social Networks, 41, 26-35.BruscoM.DoreianP.(2015a)A real-coded genetic algorithm for two-mode KL-means partitioning with application to homogeneity blockmodeling.41263510.1016/j.socnet.2014.11.007Search in Google Scholar
Brusco, M. J., & Doreian, P. (2015b). An exact algorithm for two-mode KL-means partitioning. Journal of Classification, 32, 481-515.BruscoM. J.DoreianP.(2015b)An exact algorithm for two-mode KL-means partitioning.3248151510.1007/s00357-015-9185-zSearch in Google Scholar
Brusco, M., Doreian, P., Lloyd, P., & Steinley, D. (2013a). A variable neighborhood search method for a two-mode blockmodeling problem in social network analysis, Network Science, 1 (2), 191-212.BruscoM.DoreianP.LloydP.SteinleyD.(2013a)A variable neighborhood search method for a two-mode blockmodeling problem in social network analysis,1(2)19121210.1017/nws.2013.5Search in Google Scholar
Brusco, M., Doreian, P., Mrvar, A., & Steinley, D. (2013b). An exact algorithm for blockmodeling of two-mode network data. Journal of Mathematical Sociology, 37, 61-84.BruscoM.DoreianP.MrvarA.SteinleyD.(2013b)An exact algorithm for blockmodeling of two-mode network data.37618410.1080/0022250X.2011.597278Search in Google Scholar
Brusco, M. J., Doreian, P., & Steinley, D. (2016). Biclustering methods for one-mode asymmetric matrices. Behavior Research Methods, 48, 487-502.BruscoM. J.DoreianP.SteinleyD.(2016)Biclustering methods for one-mode asymmetric matrices.4848750210.3758/s13428-015-0587-ySearch in Google Scholar
Brusco, M., & Steinley, D. (2006). Inducing a blockmodel structure for two-mode binary data using seriation procedures. Journal of Mathematical Psychology, 50, 468-477.BruscoM.SteinleyD.(2006)Inducing a blockmodel structure for two-mode binary data using seriation procedures.5046847710.1016/j.jmp.2006.05.005Search in Google Scholar
Brusco, M., & Steinley, D. (2007a). A variable neighborhood search method for generalized blockmodeling of two-mode binary matrices. Journal of Mathematical Psychology, 51, 325-338.BruscoM.SteinleyD.(2007a)A variable neighborhood search method for generalized blockmodeling of two-mode binary matrices.5132533810.1016/j.jmp.2007.07.001Search in Google Scholar
Brusco, M. J., & Steinley, D. (2007b). A comparison of heuristic procedures for minimum within-cluster sums of squares partitioning. Psychometrika, 72, 583-600.BruscoM. J.SteinleyD.(2007b)A comparison of heuristic procedures for minimum within-cluster sums of squares partitioning.7258360010.1007/s11336-007-9013-4Search in Google Scholar
Brusco, M., & Steinley, D. (2011). A tabu search heuristic for deterministic two-mode blockmodeling of binary network matrices. Psychometrika, 76, 612-633.BruscoM.SteinleyD.(2011)A tabu search heuristic for deterministic two-mode blockmodeling of binary network matrices.7661263310.1007/s11336-011-9221-9Search in Google Scholar
Ceulemans, E., & Van Mechelen, I. (2005). Hierarchical classes models for three-way three-mode binary data: interrelations and model selection. Psychometrika, 70, 461-480.CeulemansE.Van MechelenI.(2005)Hierarchical classes models for three-way three-mode binary data: interrelations and model selection.7046148010.1007/s11336-003-1067-3Search in Google Scholar
Coombs, C. H. (1964). A theory of data. New York: Wiley.CoombsC. H.(1964)New YorkWiley10.1037/h0047773Search in Google Scholar
Davis, A., Gardner, B., & Gardner, M. R. (1941). Deep south. Chicago, University of Chicago Press.DavisA.GardnerB.GardnerM. R.(1941)ChicagoUniversity of Chicago Press10.7208/chicago/9780226817996.001.0001Search in Google Scholar
Doreian, P. (1979). On delineation of small group structure. In: Hudson, H. C. (Ed.), Classifying social data (pp. 215-230), San Francisco: Jossey-Bass.DoreianP.(1979)On delineation of small group structure.In:HudsonH. C.(Ed.),(pp.215230),San FranciscoJossey-BassSearch in Google Scholar
Doreian, P., Batagelj, V., & Ferligoj, A. (2004). Generalized blockmodeling of two-mode network data. Social Networks, 26, 29-53.DoreianP.BatageljV.FerligojA.(2004)Generalized blockmodeling of two-mode network data.26295310.1016/j.socnet.2004.01.002Search in Google Scholar
Doreian, P., Batagelj, V., & Ferligoj, A. (2005). Generalized blockmodeling. Cambridge: Cambridge University Press.DoreianP.BatageljV.FerligojA.(2005)CambridgeCambridge University Press10.1017/CBO9780511584176Search in Google Scholar
Doreian, P., Lloyd, P., & Mrvar, A. (2013). Partitioning large signed two-mode networks: Problems and prospects. Social Networks, 35, 1-21.DoreianP.LloydP.MrvarA.(2013)Partitioning large signed two-mode networks: Problems and prospects.3512110.1016/j.socnet.2012.01.002Search in Google Scholar
Everett, M. G., & Borgatti, S. P. (2013). The dual-projection approach for two-mode networks. Social Networks, 35, 204-210.EverettM. G.BorgattiS. P.(2013)The dual-projection approach for two-mode networks.3520421010.1016/j.socnet.2012.05.004Search in Google Scholar
Faust, K. (1997). Centrality in affiliation networks. Social Networks, 19, 157-191.FaustK.(1997)Centrality in affiliation networks.1915719110.1016/S0378-8733(96)00300-0Search in Google Scholar
Forgy, E. W. (1965). Cluster analyses of multivariate data: Efficiency versus interpretability of classifications. Abstract in Biometrics, 21, 768-769.ForgyE. W.(1965)Cluster analyses of multivariate data: Efficiency versus interpretability of classifications. Abstract in21768769Search in Google Scholar
Freeman, L. C. (1980). Q-analysis and the structure of friendship networks. International Journal of Man-Machine Studies, 12, 367-378.FreemanL. C.(1980)Q-analysis and the structure of friendship networks.1236737810.1016/S0020-7373(80)80021-6Search in Google Scholar
Galaskiewicz, J. (1985). Social organization of an urban grants economy. New York: Academic Press.GalaskiewiczJ.(1985)New YorkAcademic PressSearch in Google Scholar
Gaul, W., & Schader, M. (1996). A new algorithm for two-mode clustering. In Bock H. & Polasek W. (Eds.), Data analysis and information systems (pp. 15-23), Berlin: Springer.GaulW.SchaderM.(1996)A new algorithm for two-mode clustering.InBockH.PolasekW.(Eds.),(pp.1523),BerlinSpringer10.1007/978-3-642-80098-6_2Search in Google Scholar
Hansen, P., & Mladenović, N. (2001). J-Means: A new local search heuristic for minimum sum of squares clustering. Pattern Recognition, 34, 405-413.HansenP.MladenovićN.(2001)J-Means: A new local search heuristic for minimum sum of squares clustering.3440541310.1016/S0031-3203(99)00216-2Search in Google Scholar
Hansohm, J. (2002). Two-mode clustering with genetic algorithms. In Gaul W. & Ritter G. (Eds.), Classification, automation and new media (pp. 87-93), Berlin: Springer.HansohmJ.(2002)Two-mode clustering with genetic algorithms.InGaulW.RitterG.(Eds.),(pp.8793),BerlinSpringer10.1007/978-3-642-55991-4_9Search in Google Scholar
Harper, F. M., & Konstan, J. A. (2015). The MovieLens datasets: History and context. ACM Transactions on Interactive and Intelligent Systems, 5 (4), Article 19, 1-19.HarperF. M.KonstanJ. A.(2015)The MovieLens datasets: History and context.5(4)Article 1911910.1145/2827872Search in Google Scholar
Hubert, L. (1974). Problems of seriation using a subject by item response matrix. Psychological Bulletin, 81, 976-983.HubertL.(1974)Problems of seriation using a subject by item response matrix.8197698310.1037/h0037348Search in Google Scholar
Hubert L, & Arabie P. (1985). Comparing partitions. Journal of Classification, 2, 195-218.HubertLArabieP.(1985)Comparing partitions.219521810.1007/BF01908075Search in Google Scholar
Lorrain, F., & White, H. C. (1971). Structural equivalence of individuals in social networks. Journal of Mathematical Sociology, 1, 49-80.LorrainF.WhiteH. C.(1971)Structural equivalence of individuals in social networks.1498010.1016/B978-0-12-442450-0.50012-2Search in Google Scholar
MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Le Cam L. M. & Neyman J. (Eds.), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1 (pp. 281-297), Berkeley, CA: University of California Press.MacQueenJ. B.(1967)Some methods for classification and analysis of multivariate observations.InLe CamL. M.NeymanJ.(Eds.),Vol. 1(pp.281297),Berkeley, CAUniversity of California PressSearch in Google Scholar
Milligan, G. W. (1980). An examination of the effects of six types of error perturbation on fifteen clustering algorithms. Psychometrika, 45, 325-342.MilliganG. W.(1980)An examination of the effects of six types of error perturbation on fifteen clustering algorithms.4532534210.1007/BF02293907Search in Google Scholar
Mische, A., & Pattison, P. (2000). Composing a civic arena: Publics, projects, and social settings. Poetics, 27, 163-194.MischeA.PattisonP.(2000)Composing a civic arena: Publics, projects, and social settings.2716319410.1016/S0304-422X(99)00024-8Search in Google Scholar
Opsahl, T. (2013). Triadic closure in two-mode networks: Redefining the global and local clustering coefficients. Social Networks, 35, 159-167.OpsahlT.(2013)Triadic closure in two-mode networks: Redefining the global and local clustering coefficients.3515916710.1016/j.socnet.2011.07.001Search in Google Scholar
Pattison, P. E. (1993). Algebraic models for social networks. New York: Cambridge University Press.PattisonP. E.(1993)New YorkCambridge University Press10.1017/CBO9780511571220Search in Google Scholar
Pattison, P. E., & Bartlett, W. K. (1982). A factorization procedure for finite algebras. Journal of Mathematical Psychology, 25, 51-81.PattisonP. E.BartlettW. K.(1982)A factorization procedure for finite algebras.25518110.1016/0022-2496(82)90046-3Search in Google Scholar
Pattison, P. E., & Brieger, R. L. (2002). Lattices and dimensional representations: matrix decompositions and ordering structures. Social Networks, 24, 423-444.PattisonP. E.BriegerR. L.(2002)Lattices and dimensional representations: matrix decompositions and ordering structures.2442344410.1016/S0378-8733(02)00015-1Search in Google Scholar
Späth, H. (1980). Cluster analysis algorithms for data reduction and classification of objects. Chichester, England: Ellis Horwood.SpäthH.(1980)Chichester, EnglandEllis HorwoodSearch in Google Scholar
Steinhaus, H. (1956). Sur la division des corps matériels en parties. Bulletin de l’Académie Polonaise des Sciences, Classe III, IV(12), 801-804.SteinhausH.(1956)Sur la division des corps matériels en parties.Classe III, IV(12)801804Search in Google Scholar
Steinley, D. (2004). Properties of the Hubert-Arabie adjusted Rand index. Psychological Methods, 9, 386-396.SteinleyD.(2004)Properties of the Hubert-Arabie adjusted Rand index.938639610.1037/1082-989X.9.3.386Search in Google Scholar
Steinley, D. (2006a). K-means clustering: A half-century synthesis. British Journal of Mathematical and Statistical Psychology, 59, 1-34.SteinleyD.(2006a)K-means clustering: A half-century synthesis.5913410.1348/000711005X48266Search in Google Scholar
Steinley, D. (2006b). Profiling local optima in K-means clustering: Developing a diagnostic technique. Psychological Methods, 11, 178-192.SteinleyD.(2006b)Profiling local optima in K-means clustering: Developing a diagnostic technique.1117819210.1037/1082-989X.11.2.178Search in Google Scholar
Trejos, J., & Castillo, W. (2000). Simulated annealing optimization for two-mode partitioning. In Gaul, W., Decker, R. (Eds.), Classification and information at the turn of the millennium (pp. 135-142), Heidelberg: Springer.TrejosJ.CastilloW.(2000)Simulated annealing optimization for two-mode partitioning.InGaulW.DeckerR.(Eds.),(pp.135142),HeidelbergSpringer10.1007/978-3-642-57280-7_15Search in Google Scholar
van Rosmalen, J., Groenen, P. J. F., Trejos, J., & Castillo, W. (2009). Optimization strategies for two-mode partitioning. Journal of Classification, 26, 155-181.van RosmalenJ.GroenenP. J. F.TrejosJ.CastilloW.(2009)Optimization strategies for two-mode partitioning.2615518110.1007/s00357-009-9031-2Search in Google Scholar
Vichi, M. (2001). Double K-means clustering for simultaneous classification of objects and variables. In Borra, S., Rocchi, R., Schader, M. (Eds.), Advances in classification and data analysis – studies in classification, data analysis and knowledge organization (pp. 43-52), Heidelberg: Springer.VichiM.(2001)Double K-means clustering for simultaneous classification of objects and variables.InBorraS.RocchiR.SchaderM.(Eds.),(pp.4352),HeidelbergSpringerSearch in Google Scholar
Wasserman, S., & Faust, K. (1994). Social network analysis: methods and applications. Cambridge: Cambridge University Press.WassermanS.FaustK.(1994)CambridgeCambridge University Press10.1017/CBO9780511815478Search in Google Scholar
Wilderjans, T. F., Ceulemans, E., & Meers, K. (2013). CHull: A generic convex hull based model selection method. Behavior Research Methods, 45, 1-15.WilderjansT. F.CeulemansE.MeersK.(2013)CHull: A generic convex hull based model selection method.4511510.3758/s13428-012-0238-5Search in Google Scholar
Xu, W., Liu, X, & Gong, Y. (2001). Document clustering based on non-negative matrix factorization. Proceedings of the 26th ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 267-273.XuW.LiuXGongY.(2001)Document clustering based on non-negative matrix factorization.pp.26727310.1145/860435.860485Search in Google Scholar