{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:19:56Z","timestamp":1761581996857,"version":"3.37.3"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T00:00:00Z","timestamp":1597190400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T00:00:00Z","timestamp":1597190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["..."],"award-info":[{"award-number":["..."]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["..."],"award-info":[{"award-number":["..."]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["..."],"award-info":[{"award-number":["..."]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001804","name":"Canada Research Chairs","doi-asserted-by":"publisher","award":["..."],"award-info":[{"award-number":["..."]}],"id":[{"id":"10.13039\/501100001804","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Classif"],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1007\/s00357-020-09371-4","type":"journal-article","created":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T08:04:25Z","timestamp":1597219465000},"page":"264-279","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8539-6347","authenticated-orcid":false,"given":"Sharon M.","family":"McNicholas","sequence":"first","affiliation":[]},{"given":"Paul D.","family":"McNicholas","sequence":"additional","affiliation":[]},{"given":"Daniel A.","family":"Ashlock","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,12]]},"reference":[{"key":"9371_CR1","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1016\/j.patrec.2013.02.008","volume":"34","author":"JL Andrews","year":"2013","unstructured":"Andrews, J.L., & McNicholas, P.D. (2013). Using evolutionary algorithms for model-based clustering. Pattern Recognition Letters, 34, 987\u2013992.","journal-title":"Pattern Recognition Letters"},{"key":"9371_CR2","volume-title":"Evolutionary Computation for Modeling and Optimization","author":"D Ashlock","year":"2010","unstructured":"Ashlock, D. (2010). Evolutionary Computation for Modeling and Optimization. Springer-Verlag: New York."},{"issue":"1","key":"9371_CR3","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1002\/cjs.11308","volume":"45","author":"L Bagnato","year":"2017","unstructured":"Bagnato, L., Punzo, A., & Zoia, M.G. (2017). The multivariate leptokurtic-normal distribution and its application in model-based clustering. Canadian Journal of Statistics, 45(1), 95\u2013119.","journal-title":"Canadian Journal of Statistics"},{"issue":"7","key":"9371_CR4","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1109\/34.865189","volume":"22","author":"C Biernacki","year":"2000","unstructured":"Biernacki, C., Celeux, G., & Govaert, G. (2000). Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(7), 719\u2013725.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"9371_CR5","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.csda.2012.12.008","volume":"71","author":"C Bouveyron","year":"2014","unstructured":"Bouveyron, C., & Brunet-Saumard, C. (2014). Model-based clustering of high-dimensional data: a review. Computational Statistics and Data Analysis, 71, 52\u201378.","journal-title":"Computational Statistics and Data Analysis"},{"issue":"2","key":"9371_CR6","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s11634-013-0139-1","volume":"8","author":"RP Browne","year":"2014","unstructured":"Browne, R.P., & McNicholas, P.D. (2014a). Estimating common principal components in high dimensions. Advances in Data Analysis and Classification, 8(2), 217\u2013226.","journal-title":"Advances in Data Analysis and Classification"},{"key":"9371_CR7","unstructured":"Browne, R.P., & McNicholas, P.D. (2014b). Mixture: mixture models for clustering and classification. R package version 1.1."},{"issue":"2","key":"9371_CR8","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s11222-012-9364-2","volume":"24","author":"RP Browne","year":"2014","unstructured":"Browne, R.P., & McNicholas, P.D. (2014c). Orthogonal Stiefel manifold optimization for eigen-decomposed covariance parameter estimation in mixture models. Statistics and Computing, 24(2), 203\u2013210.","journal-title":"Statistics and Computing"},{"issue":"3","key":"9371_CR9","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/0167-9473(92)90042-E","volume":"14","author":"G Celeux","year":"1992","unstructured":"Celeux, G., & Govaert, G. (1992). A classification EM algorithm for clustering and two stochastic versions. Computational Statistics and Data Analysis, 14 (3), 315\u2013332.","journal-title":"Computational Statistics and Data Analysis"},{"issue":"5","key":"9371_CR10","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1016\/0031-3203(94)00125-6","volume":"28","author":"G Celeux","year":"1995","unstructured":"Celeux, G., & Govaert, G. (1995). Gaussian parsimonious clustering models. Pattern Recognition, 28(5), 781\u2013793.","journal-title":"Pattern Recognition"},{"key":"9371_CR11","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1080\/01621459.1998.10474110","volume":"93","author":"A Dasgupta","year":"1998","unstructured":"Dasgupta, A., & Raftery, A.E. (1998). Detecting features in spatial point processes with clutter via model-based clustering. Journal of the American Statistical Association, 93, 294\u2013302.","journal-title":"Journal of the American Statistical Association"},{"issue":"1","key":"9371_CR12","first-page":"1","volume":"55","author":"N Dean","year":"2006","unstructured":"Dean, N., Murphy, T.B., & Downey, G. (2006). Using unlabelled data to update classification rules with applications in food authenticity studies. Journal of the Royal Statistical Society: Series C, 55(1), 1\u201314.","journal-title":"Journal of the Royal Statistical Society: Series C"},{"issue":"1","key":"9371_CR13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster, A.P., Laird, N.M., & Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1\u201338.","journal-title":"Journal of the Royal Statistical Society: Series B"},{"key":"9371_CR14","unstructured":"Flury, B. (2012). Flury: data sets from flury, 1997. R package version 0.1\u20133."},{"key":"9371_CR15","first-page":"189","volume":"25","author":"M Forina","year":"1986","unstructured":"Forina, M., Armanino, C., Castino, M., & Ubigli, M. (1986). Multivariate data analysis as a discriminating method of the origin of wines. Vitis, 25, 189\u2013201.","journal-title":"Vitis"},{"issue":"458","key":"9371_CR16","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1198\/016214502760047131","volume":"97","author":"C Fraley","year":"2002","unstructured":"Fraley, C., & Raftery, A.E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611\u2013631.","journal-title":"Journal of the American Statistical Association"},{"key":"9371_CR17","unstructured":"Fraley, C., Raftery, A.E., Murphy, T.B., & Scrucca, L. (2012). Mclust version 4 for R: Normal mixture modeling for model-based clustering, classification, and density estimation. Technical Report 597, Department of Statistics, University of Washington, Seattle, WA."},{"key":"9371_CR18","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.patcog.2018.02.025","volume":"80","author":"MPB Gallaugher","year":"2018","unstructured":"Gallaugher, M.P.B., & McNicholas, P.D. (2018). Finite mixtures of skewed matrix variate distributions. Pattern Recognition, 80, 83\u201393.","journal-title":"Pattern Recognition"},{"issue":"2","key":"9371_CR19","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1007\/s00357-018-9280-z","volume":"36","author":"MPB Gallaugher","year":"2019","unstructured":"Gallaugher, M.P.B., & McNicholas, P.D. (2019). On fractionally-supervised classification: Weight selection and extension to the multivariate t-distribution. Journal of Classification, 36(2), 232\u2013265.","journal-title":"Journal of Classification"},{"issue":"2","key":"9371_CR20","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s11634-019-00377-4","volume":"14","author":"MPB Gallaugher","year":"2020","unstructured":"Gallaugher, M.P.B., & McNicholas, P.D. (2020a). Mixtures of skewed matrix variate bilinear factor analyzers. Advances in Data Analysis and Classification, 14(2), 415\u2013434.","journal-title":"Advances in Data Analysis and Classification"},{"key":"9371_CR21","doi-asserted-by":"crossref","unstructured":"Gallaugher, M.P.B., & McNicholas, P.D. (2020b). Parsimonious mixtures of matrix variate bilinear factor analyzers. In Imaizumi, T., Nakayama, A., & Yokoyama, S. (Eds.) Advanced studies in behaviormetrics and data science: Essays in honor of Akinori Okada (pp. 177\u2013196). Singapore: Springer.","DOI":"10.1007\/978-981-15-2700-5_11"},{"key":"9371_CR22","volume-title":"The EM algorithm for factor analyzers Technical Report CRG-TR-96-1","author":"Z Ghahramani","year":"1997","unstructured":"Ghahramani, Z., & Hinton, G.E. (1997). The EM algorithm for factor analyzers Technical Report CRG-TR-96-1. Toronto: University Of Toronto."},{"issue":"1","key":"9371_CR23","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF01908075","volume":"2","author":"L Hubert","year":"1985","unstructured":"Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193\u2013218.","journal-title":"Journal of Classification"},{"issue":"1","key":"9371_CR24","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1198\/0003130042836","volume":"58","author":"DL Hunter","year":"2004","unstructured":"Hunter, D.L., & Lange, K. (2004). A tutorial on MM algorithms. The American Statistician, 58(1), 30\u201337.","journal-title":"The American Statistician"},{"issue":"4","key":"9371_CR25","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1198\/106186004X12425","volume":"13","author":"C Hurley","year":"2004","unstructured":"Hurley, C. (2004). Clustering visualizations of multivariate data. Journal of Computational and Graphical Statistics, 13(4), 788\u2013806.","journal-title":"Journal of Computational and Graphical Statistics"},{"issue":"431","key":"9371_CR26","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1080\/01621459.1995.10476592","volume":"90","author":"RE Kass","year":"1995","unstructured":"Kass, R.E., & Wasserman, L. (1995). A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion. Journal of the American Statistical Association, 90(431), 928\u2013934.","journal-title":"Journal of the American Statistical Association"},{"issue":"3","key":"9371_CR27","doi-asserted-by":"publisher","first-page":"1350","DOI":"10.1214\/aos\/1176348772","volume":"20","author":"BG Leroux","year":"1992","unstructured":"Leroux, B.G. (1992). Consistent estimation of a mixing distribution. The Annals of Statistics, 20(3), 1350\u20131360.","journal-title":"The Annals of Statistics"},{"key":"9371_CR28","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1177\/1471082X17718119","volume":"18","author":"T-I Lin","year":"2018","unstructured":"Lin, T.-I., Wang, W.-L., McLachlan, G.J., & Lee, S.X. (2018). Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution. Statistical Modelling, 18, 50\u201372.","journal-title":"Statistical Modelling"},{"issue":"11","key":"9371_CR29","doi-asserted-by":"publisher","first-page":"5352","DOI":"10.1016\/j.csda.2006.07.020","volume":"51","author":"C McGrory","year":"2007","unstructured":"McGrory, C., & Titterington, D. (2007). Variational approximations in Bayesian model selection for finite mixture distributions. Computational Statistics and Data Analysis, 51(11), 5352\u20135367.","journal-title":"Computational Statistics and Data Analysis"},{"key":"9371_CR30","doi-asserted-by":"crossref","unstructured":"McLachlan, G.J. (1982). The classification and mixture maximum likelihood approaches to cluster analysis. In Krishnaiah, P.R., & Kanal, L. (Eds.) Handbook of statistics, vol. 2, pp 199\u2013208. Amsterdam: North-Holland.","DOI":"10.1016\/S0169-7161(82)02012-4"},{"key":"9371_CR31","doi-asserted-by":"publisher","DOI":"10.1002\/0471725293","volume-title":"Discriminant analysis and statistical pattern recognition","author":"GJ McLachlan","year":"1992","unstructured":"McLachlan, G.J. (1992). Discriminant analysis and statistical pattern recognition. New Jersey: John Wiley & Sons."},{"key":"9371_CR32","doi-asserted-by":"publisher","DOI":"10.1002\/0471721182","volume-title":"Finite mixture models","author":"GJ McLachlan","year":"2000","unstructured":"McLachlan, G.J., & Peel, D. (2000a). Finite mixture models. New York: John Wiley & Sons."},{"key":"9371_CR33","unstructured":"McLachlan, G.J., & Peel, D. (2000b). Mixtures of factor analyzers. In Proceedings of the seventh international conference on machine learning, San Francisco, pp 599\u2013606. Morgan Kaufmann."},{"issue":"5","key":"9371_CR34","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1016\/j.jspi.2009.11.006","volume":"140","author":"PD McNicholas","year":"2010","unstructured":"McNicholas, P.D. (2010). Model-based classification using latent Gaussian mixture models. Journal of Statistical Planning and Inference, 140(5), 1175\u20131181.","journal-title":"Journal of Statistical Planning and Inference"},{"key":"9371_CR35","doi-asserted-by":"publisher","DOI":"10.1201\/9781315373577","volume-title":"Mixture model-based classification","author":"PD McNicholas","year":"2016","unstructured":"McNicholas, P.D. (2016a). Mixture model-based classification. Boca Raton: Chapman & Hall\/CRC Press."},{"issue":"3","key":"9371_CR36","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s00357-016-9211-9","volume":"33","author":"PD McNicholas","year":"2016","unstructured":"McNicholas, P.D. (2016b). Model-based clustering. Journal of Classification, 33(3), 331\u2013373.","journal-title":"Journal of Classification"},{"issue":"3","key":"9371_CR37","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s11222-008-9056-0","volume":"18","author":"PD McNicholas","year":"2008","unstructured":"McNicholas, P.D., & Murphy, T.B. (2008). Parsimonious gaussian mixture models. Statistics and Computing, 18(3), 285\u2013296.","journal-title":"Statistics and Computing"},{"issue":"21","key":"9371_CR38","doi-asserted-by":"publisher","first-page":"2705","DOI":"10.1093\/bioinformatics\/btq498","volume":"26","author":"PD McNicholas","year":"2010","unstructured":"McNicholas, P.D., & Murphy, T.B. (2010). Model-based clustering of microarray expression data via latent gaussian mixture models. Bioinformatics, 26 (21), 2705\u20132712.","journal-title":"Bioinformatics"},{"key":"9371_CR39","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.jmva.2018.04.007","volume":"167","author":"V Melnykov","year":"2018","unstructured":"Melnykov, V., & Zhu, X. (2018). On model-based clustering of skewed matrix data. Journal of Multivariate Analysis, 167, 181\u2013194.","journal-title":"Journal of Multivariate Analysis"},{"issue":"1","key":"9371_CR40","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/s11634-018-0326-1","volume":"13","author":"V Melnykov","year":"2019","unstructured":"Melnykov, V., & Zhu, X. (2019). Studying crime trends in the USA over the years 2000\u20132012. Advances in Data Analysis and Classification, 13(1), 325\u2013341.","journal-title":"Advances in Data Analysis and Classification"},{"key":"9371_CR41","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.csda.2018.12.001","volume":"132","author":"K Morris","year":"2019","unstructured":"Morris, K., Punzo, A., McNicholas, P.D., & Browne, R.P. (2019). Asymmetric clusters and outliers: Mixtures of multivariate contaminated shifted asymmetric Laplace distributions. Computational Statistics and Data Analysis, 132, 145\u2013166.","journal-title":"Computational Statistics and Data Analysis"},{"issue":"2","key":"9371_CR42","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1007\/s00357-019-9309-y","volume":"37","author":"PM Murray","year":"2020","unstructured":"Murray, P.M., Browne, R.P., & McNicholas, P.D. (2020). Mixtures of hidden truncation hyperbolic factor analyzers. Journal of Classification, 37(2), 366\u2013379.","journal-title":"Journal of Classification"},{"issue":"1","key":"9371_CR43","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.patrec.2018.07.003","volume":"112","author":"A Pesevski","year":"2018","unstructured":"Pesevski, A., Franczak, B.C., & McNicholas, P.D. (2018). Subspace clustering with the multivariate-t distribution. Pattern Recognition Letters, 112(1), 297\u2013302.","journal-title":"Pattern Recognition Letters"},{"key":"9371_CR44","volume-title":"R: a language and environment for statistical computing","author":"R Core Team","year":"2018","unstructured":"R Core Team. (2018). R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing."},{"issue":"336","key":"9371_CR45","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1080\/01621459.1971.10482356","volume":"66","author":"WM Rand","year":"1971","unstructured":"Rand, W.M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66(336), 846\u2013850.","journal-title":"Journal of the American Statistical Association"},{"key":"9371_CR46","doi-asserted-by":"publisher","first-page":"894","DOI":"10.1080\/01621459.1997.10474044","volume":"92","author":"K Roeder","year":"1997","unstructured":"Roeder, K., & Wasserman, L. (1997). Practical Bayesian density estimation using mixtures of normals. Journal of the American Statistical Association, 92, 894\u2013902.","journal-title":"Journal of the American Statistical Association"},{"key":"9371_CR47","doi-asserted-by":"crossref","unstructured":"Sarkar, S., Zhu, X., Melnykov, V., & Ingrassia, S. (2020). On parsimonious models for modeling matrix data. Computational Statistics and Data Analysis, 142.","DOI":"10.1016\/j.csda.2019.106822"},{"key":"9371_CR48","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1214\/aos\/1176344136","volume":"6","author":"G Schwarz","year":"1978","unstructured":"Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461\u2013464.","journal-title":"The Annals of Statistics"},{"key":"9371_CR49","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316849","volume-title":"Multivariate density estimation","author":"DW Scott","year":"1992","unstructured":"Scott, D.W. (1992). Multivariate density estimation. New York: Wiley."},{"key":"9371_CR50","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1037\/1082-989X.9.3.386","volume":"9","author":"D Steinley","year":"2004","unstructured":"Steinley, D. (2004). Properties of the Hubert-Arabie adjusted Rand index. Psychological Methods, 9, 386\u2013396.","journal-title":"Psychological Methods"},{"issue":"2","key":"9371_CR51","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1007\/s11634-014-0165-7","volume":"8","author":"S Subedi","year":"2014","unstructured":"Subedi, S., & McNicholas, P.D. (2014). Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions. Advances in Data Analysis and Classification, 8(2), 167\u2013193.","journal-title":"Advances in Data Analysis and Classification"},{"key":"9371_CR52","doi-asserted-by":"publisher","unstructured":"Subedi, S., & McNicholas, P.D. (2019). A variational approximations-DIC rubric for parameter estimation and mixture model selection within a family setting. Journal of Classification. To appear. https:\/\/doi.org\/10.1007\/s00357-019-09351-3.","DOI":"10.1007\/s00357-019-09351-3"},{"key":"9371_CR53","volume-title":"Statistical analysis of finite mixture distributions","author":"DM Titterington","year":"1985","unstructured":"Titterington, D.M., Smith, A.F.M. , & Makov, U.E. (1985). Statistical analysis of finite mixture distributions. Chichester: John Wiley & Sons."},{"issue":"1","key":"9371_CR54","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/s00357-019-09319-3","volume":"36","author":"C Tortora","year":"2019","unstructured":"Tortora, C., Franczak, B.C., Browne, R.P., & McNicholas, P.D. (2019). A mixture of coalesced generalized hyperbolic distributions. Journal of Classification, 36(1), 26\u201357.","journal-title":"Journal of Classification"},{"issue":"1","key":"9371_CR55","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1037\/a0020144","volume":"16","author":"JK Vermunt","year":"2011","unstructured":"Vermunt, J.K. (2011). K-means may perform as well as mixture model clustering but may also be much worse: Comment on Steinley and Brusco. Psychological Methods, 16(1), 82\u201388.","journal-title":"Psychological Methods"},{"issue":"3","key":"9371_CR56","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/s00357-015-9188-9","volume":"32","author":"I Vrbik","year":"2015","unstructured":"Vrbik, I., & McNicholas, P.D. (2015). Fractionally-supervised classification. Journal of Classification, 32(3), 359\u2013381.","journal-title":"Journal of Classification"},{"issue":"521","key":"9371_CR57","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1080\/01621459.2017.1330202","volume":"113","author":"ML Wallace","year":"2018","unstructured":"Wallace, M.L., Buysse, D.J., Germain, A., Hall, M.H., & Iyengar, S. (2018). Variable selection for skewed model-based clustering: Application to the identification of novel sleep phenotypes. Journal of the American Statistical Association, 113(521), 95\u2013110.","journal-title":"Journal of the American Statistical Association"},{"issue":"3","key":"9371_CR58","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TPAMI.2018.2885760","volume":"42","author":"Y Wei","year":"2020","unstructured":"Wei, Y., Tang, Y., & McNicholas, P.D. (2020). Flexible high-dimensional unsupervised learning with missing data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(3), 610\u2013621.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"9371_CR59","doi-asserted-by":"crossref","unstructured":"Wolfe, J.H. (1965). A computer program for the maximum-likelihood analysis of types. USNPRA Technical Bulletin 65-15, U.S.Naval Personal Research Activity, San Diego.","DOI":"10.21236\/AD0620026"}],"container-title":["Journal of Classification"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00357-020-09371-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00357-020-09371-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00357-020-09371-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T18:01:06Z","timestamp":1723399266000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00357-020-09371-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,12]]},"references-count":59,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["9371"],"URL":"https:\/\/doi.org\/10.1007\/s00357-020-09371-4","relation":{},"ISSN":["0176-4268","1432-1343"],"issn-type":[{"type":"print","value":"0176-4268"},{"type":"electronic","value":"1432-1343"}],"subject":[],"published":{"date-parts":[[2020,8,12]]},"assertion":[{"value":"12 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}