Ahmad,, A., & Dey,, L. (2007). A k‐mean clustering algorithm for mixed numeric and categorical data. Data %26 Knowledge Engineering, 63(2), 503–527. https://doi.org/10.1016/j.datak.2007.03.016
Alfons,, A. (2016). robustHD: Robust Methods for High‐Dimensional Data [Computer Software manual]. R package version 0.5.1. Retrieved from https://CRAN.R‐project.org/package=robustHD
Alfons,, A., Croux,, C., & Gelper,, S. (2016). Robust groupwise least angle regression. Computational Statistics %26 Data Analysis, 93, 421–435. https://doi.org/10.1016/j.csda.2015.02.007
Aşan,, Z., & Greenacre,, M. (2011). Biplots of fuzzy coded data. Fuzzy Sets and Systems, 183(1), 57–71. https://doi.org/10.1016/j.fss.2011.03.007
Audigier,, V., Husson,, F., & Josse,, J. (2016). A principal component method to impute missing values for mixed data. Advances in Data Analysis and Classification, 10(1), 5–26. https://doi.org/10.1007/s11634-014-0195-1
Bock,, H. (1987). On the interface between cluster analysis, principal component analysis, and multidimensional scaling. In H. Bozdogan, & A. Gupta, (Eds.), Multivariate statistical modeling and data analysis (pp. 17–34). Dordrecht: Springer. https://doi.org/10.1007/978-94-009-3977-6_2
Browne,, R. P., & McNicholas,, P. D. (2012). Model‐based clustering, classification, and discriminant analysis of data with mixed type. Journal of Statistical Planning and Inference, 142(11), 2976–2984. https://doi.org/10.1016/j.jspi.2012.05.001
Browne,, R. P., & McNicholas,, P. D. (2015). A mixture of generalized hyperbolic distributions. The Canadian Journal of Statistics, 43(2), 176–198. https://doi.org/10.1002/cjs.11246
Bushel,, P. R., Wolfinger,, R. D., & Gibson,, G. (2007). Simultaneous clustering of gene expression data with clinical chemistry and pathological evaluations reveals phenotypic prototypes. BMC Systems Biology, 1(1), 15. https://doi.org/10.1186/1752-0509-1-15
Cai,, J. H., Song,, X. Y., Lam,, K. H., & Ip,, E. H. S. (2011). A mixture of generalized latent variable models for mixed mode and heterogeneous data. Computational Statistics and Data Analysis, 55(11), 2889–2907. https://doi.org/10.1016/j.csda.2011.05.011
Chavent,, M., Kuentz‐Simonet,, V., Labenne,, A., & Saracco,, J. (2017). Multivariate analysis of mixed data: The PCAmixdata R package. arXiv preprint arXiv:1411.4911.
de Leeuw,, J., & van Rijckevorsel,, J. (1980). HOMALS and PRINCALS‐some generalizations of principal components analysis. Data Analysis and Informatics, 2, 231–242.
De Soete,, G., & Carroll,, J. D. (1994). K‐means clustering in a low‐dimensional Euclidean space. In E. Diday,, Y. Lechevallier,, M. Schader,, P. Bertrand,, & B. Burtschy, (Eds.), New approaches in classification and data analysis (pp. 212–219). Berlin, Germany: Springer‐Verlag. https://doi.org/10.1007/978-3-642-51175-2_24
Everitt,, B. S. (1988). A finite mixture model for the clustering of mixed‐mode data. Statistics %26 Probability Letters, 6(5), 305–309. https://doi.org/10.1016/0167-7152(88)90004-1
Fong,, D. Y., & Yip,, P. (1993). An EM algorithm for a mixture model of count data. Statistics %26 Probability Letters, 17(1), 53–60. https://doi.org/10.1016/0167-7152(93)90195-O
Foss,, A., & Markatou,, M. (2018). kamila: Clustering mixed‐type data in R and Hadoop. Journal of Statistical Software, 83(1), 1–44. https://doi.org/10.18637/jss.v083.i13
Foss,, A., Markatou,, M., & Ray,, B. (2018). Distance metrics and clustering methods for mixed‐type data. International Statistical Review. https://doi.org/10.1111/insr.12274
Foss,, A., Markatou,, M., Ray,, B., & Heching,, A. (2016). A semiparametric method for clustering mixed data. Machine Learning, 105(3), 419–458. https://doi.org/10.1007/s10994-016-5575-7
Fraley,, C., & Raftery,, A. E. (2002). Model‐based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631. https://doi.org/10.1198/016214502760047131
Gifi,, A. (1990). Nonlinear multivariate analysis. Chichester, England: Wiley.
Gower,, J. (1971). A general coefficient of similarity and some of its properties. Biometrics, 27(4), 857–871. https://doi.org/10.2307/2528823
Greenacre,, M. (2014). Data doubling and fuzzy coding. In M. Greenacre, & J. Blasius, (Eds.), Visualization and verbalization of data (pp. 239–253). Boca Raton, FL: CRC Press.
Greenacre,, M. (2017). Correspondence analysis in practice. Boca Raton, FL: Chapman and Hall/CRC.
Hennig,, C. (2015). What are the true clusters? Pattern Recognition Letters, 64, 53–62.
Hennig,, C., & Liao,, T. F. (2013). How to find an appropriate clustering for mixed‐type variables with application to socio‐economic stratification. Journal of the Royal Statistical Society: Series C: Applied Statistics, 62(3), 309–369. https://doi.org/10.1111/j.1467-9876.2012.01066.x
Hennig,, C., Meila,, M., Murtagh,, F., & Rocci,, R. (2015). Handbook of cluster analysis. Boca Raton, FL: CRC Press.
Hill,, M., & Smith,, A. (1976). Principal component analysis of taxonomic data with multi‐state discrete characters. Taxon, 25, 249–255. https://doi.org/10.2307/1219449
Huang,, Z. (1998). Extensions to the k‐means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery, 2(3), 283–304. https://doi.org/10.1023/A:1009769707641
Hubert,, L., & Arabie,, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193–218. https://doi.org/10.1007/BF01908075
Hunt,, L., & Jorgensen,, M. (2011). Clustering mixed data. WIREs Data Mining and Knowledge Discovery, 1(4), 352–361. https://doi.org/10.1002/widm.33
Hwang,, H., Dillon,, W. R., & Takane,, Y. (2006). An extension of multiple correspondence analysis for identifying heterogenous subgroups of respondents. Psychometrika, 71, 161–171. https://doi.org/10.1007/s11336-004-1173-x
Iodice D`Enza,, A., & Palumbo,, F. (2013). Iterative factor clustering of binary data. Computational Statistics, 28(2), 789–807. https://doi.org/10.1007/s00180-012-0329-x
Ji,, J., Bai,, T., Zhou,, C., Ma,, C., & Wang,, Z. (2013). An improved k‐prototypes clustering algorithm for mixed numeric and categorical data. Neurocomputing, 120, 590–596. https://doi.org/10.1016/j.neucom.2013.04.011
Jolliffe,, J. (2002). Principal component analysis. New York: Springer‐Verlag.
Kaufman,, L., & Rousseeuw,, P. J. (1990). Finding groups in data: An Introduction to cluster analysis. Hoboken, NJ: John Wiley %26 Sons.
Kiers,, H. A. (1991). Simple structure in component analysis techniques for mixtures of qualitative and quantitative variables. Psychometrika, 56(2), 197–212. https://doi.org/10.1007/BF02294458
Laliberté,, E., Legendre,, P., & Shipley,, B. (2014). FD: measuring functional diversity from multiple traits, and other tools for functional Ecology [Computer software manual]. R package version 1.0‐12. Retrieved from: https://CRAN.R‐project.org/package=FD.
Lawrence,, C. J., & Krzanowski,, W. J. (1996). Mixture separation for mixed‐mode data. Statistics and Computing, 6(1), 85–92. https://doi.org/10.1007/BF00161577
Lê,, S., Josse,, J., & Husson,, F. (2008). FactoMineR: An R package for multivariate analysis. Journal of Statistical Software, 25(1), 1–18. https://doi.org/10.18637/jss.v025.i01
Lin,, T. I. (2009). Maximum likelihood estimation for multivariate skew normal mixture models. Journal of Multivariate Analysis, 100(2), 257–265. https://doi.org/10.1016/j.jmva.2008.04.010
Maechler,, M., Rousseeuw,, P., Struyf,, A., Hubert,, M., & Hornik,, K. (2018). cluster: Cluster analysis basics and extensions [Computer Software manual]. R package version 2.0.7‐1. Retrieved from https://CRAN.R‐project.org/package=cluster.
Markos,, A., Iodice D`Enza,, A., & van de Velden,, M. (2018). clustrd: Methods for joint dimension reduction and clustering [Computer Software manual]. R package version 1.2.3. Retrieved from https://CRAN.R‐project.org/package=clustrd.
Mayrose,, I., Friedman,, N., & Pupko,, T. (2005). A gamma mixture model better accounts for among site rate heterogeneity. Bioinformatics, 21(Suppl 2, 151–158. https://doi.org/10.1093/bioinformatics/bti1125
McParland,, D., & Gormley,, I. C. (2016). Model based clustering for mixed data: ClustMD. Advances in Data Analysis and Classification, 10(2), 155–169. https://doi.org/10.1007/s11634-016-0238-x
Mirkin,, B. (2005). Clustering: A data recovery approach. London: CRC Press.
Modha,, D. S., & Spangler,, W. S. (2003). Feature weighting in k‐means clustering. Machine Learning, 52(3), 217–237. https://doi.org/10.1023/A:1024016609528
Pagès,, J. (2004). Analyse factorielle de données mixtes. Revue de Statistique Appliquée, 52(4), 93–111.
Pathberiya,, H. A. (2016). DisimForMixed: Calculate dissimilarity matrix for dataset with mixed attributes [Computer software manual]. R package version 0.2. Retrieved from https://CRAN.R‐project.org/package=DisimForMixed.
Podani,, J. (1999). Extending Gower`s general coefficient of similarity to ordinal characters. Taxon, 48, 331–340. https://doi.org/10.2307/1224438
Szepannek,, G. (2017). clustMixType: k‐prototypes clustering for mixed variable‐type data [Computer software manual]. R package version 0.1‐29. Retrieved from https://CRAN.R‐project.org/package=clustMixType
Timmerman,, M. E., Ceulemans,, E., Kiers,, H. A., & Vichi,, M. (2010). Factorial and reduced k‐means reconsidered. Computational Statistics %26 Data Analysis, 54(7), 1858–1871.
Van Buuren,, S., & Heiser,, W. J. (1989). Clustering n objects into k groups under optimal scaling of variables. Psychometrika, 54(4), 699–706. https://doi.org/10.1007/BF02296404
van Dam,, J. W., & van de Velden,, M. (2015). Online profiling and clustering of facebook users. Decision Support Systems, 70, 60–72. https://doi.org/10.1016/j.dss.2014.12.001
van de Velden,, M., Iodice D`Enza,, A., & Palumbo,, F. (2017). Cluster correspondence analysis. Psychometrika, 82(1), 158–185. https://doi.org/10.1007/s11336-016-9514-0
van Rijckevorsel,, J. (1988). Fuzzy coding and B‐splines. In J. van Rijckevorsel, & J. de Leeuw, (Eds.), Component and correspondence analysis. Dimension reduction by functional approximation (pp. 33–54). Chichester, England: Wiley.
Vichi,, M., & Kiers,, H. A. (2001). Factorial k‐means analysis for two‐way data. Computational Statistics %26 Data Analysis, 37(1), 49–64. https://doi.org/10.1016/S0167-9473(00)00064-5
Vichi,, M., Vicari,, D., & Kiers,, H. (2009). Clustering and dimensional reduction for mixed variables. Behaviormetrika 2018. Unpublished manuscript
Willse,, A., & Boik,, R. J. (1999). Identifiable finite mixtures of location models for clustering mixed‐mode data. Statistics and Computing, 9(2), 111–121. https://doi.org/10.1023/A:1008842432747
Yamamoto,, M., & Hwang,, H. (2014). A general formulation of cluster analysis with dimension reduction and subspace separation. Behaviormetrika, 41(1), 115–129.