{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T11:13:30Z","timestamp":1777634010950,"version":"3.51.4"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2018,11,14]],"date-time":"2018-11-14T00:00:00Z","timestamp":1542153600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017R1D1A1B04030695"],"award-info":[{"award-number":["NRF-2017R1D1A1B04030695"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["2017R1D1A1B05028565"],"award-info":[{"award-number":["2017R1D1A1B05028565"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Adv Data Anal Classif"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1007\/s11634-018-0350-1","type":"journal-article","created":{"date-parts":[[2018,11,14]],"date-time":"2018-11-14T03:09:56Z","timestamp":1542164996000},"page":"991-1018","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Convex clustering for binary data"],"prefix":"10.1007","volume":"13","author":[{"given":"Hosik","family":"Choi","sequence":"first","affiliation":[]},{"given":"Seokho","family":"Lee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,11,14]]},"reference":[{"key":"350_CR1","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1137\/080716542","volume":"2","author":"A Beck","year":"2009","unstructured":"Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2:183\u2013202","journal-title":"SIAM J Imaging Sci"},{"key":"350_CR2","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/BF00048682","volume":"44","author":"D B\u00f6hning","year":"1992","unstructured":"B\u00f6hning D (1992) Multinomial logistic regression algorithm. Ann Inst Stat Math 44:197\u2013200","journal-title":"Ann Inst Stat Math"},{"key":"350_CR3","first-page":"1","volume":"3","author":"S Boyd","year":"2011","unstructured":"Boyd S, Parikh N, Chu E, Peleato B, Eckstein J (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trend${}^{\\textregistered }$ Mach Learn 3:1\u2013122","journal-title":"Found Trend${}^{\\textregistered }$ Mach Learn"},{"key":"350_CR4","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1080\/10618600.2014.948181","volume":"24","author":"EC Chi","year":"2015","unstructured":"Chi EC, Lange K (2015) Splitting methods for convex clustering. J Comput Graph Stat 24:994\u20131013","journal-title":"J Comput Graph Stat"},{"key":"350_CR5","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1214\/009053604000000067","volume":"32","author":"B Efron","year":"2004","unstructured":"Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression. Ann Stat 32:407\u2013499","journal-title":"Ann Stat"},{"key":"350_CR6","doi-asserted-by":"crossref","first-page":"85","DOI":"10.6339\/JDS.2005.03(1).192","volume":"3","author":"H Finch","year":"2005","unstructured":"Finch H (2005) Comparison of distance measures in cluster analysis with dichotomous data. J Data Sci 3:85\u2013100","journal-title":"J Data Sci"},{"key":"350_CR7","doi-asserted-by":"publisher","first-page":"1588","DOI":"10.1137\/120896219","volume":"7","author":"T Goldstein","year":"2014","unstructured":"Goldstein T, O\u2019Donoghue B, Setzer S, Baraniuk R (2014) Fast alternating direction optimization methods. SIAM J Imaging Sci 7:1588\u20131623","journal-title":"SIAM J Imaging Sci"},{"key":"350_CR8","volume-title":"Matrix computations","author":"GH Golub","year":"1996","unstructured":"Golub GH, Van Loan CF (1996) Matrix computations, 3rd edn. Johns Hopkins University Press, Baltimore","edition":"3"},{"key":"350_CR9","doi-asserted-by":"crossref","unstructured":"Hallac D, Leskovec J, Boyd S (2015) Network lasso: clustering and optimization in large graphs. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 387\u2013396","DOI":"10.1145\/2783258.2783313"},{"key":"350_CR10","unstructured":"Hocking TD, Joullin A, Bach F, Vert J-P (2011) Cluterpath: an algorithm for clustering using convex fusion penalties. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 754\u2013762"},{"key":"350_CR11","volume-title":"Principal component analysis","author":"IT Jolliffe","year":"2012","unstructured":"Jolliffe IT (2012) Principal component analysis, 2nd edn. Springer, New York","edition":"2"},{"key":"350_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-4182-7","volume-title":"Optimization","author":"K Lange","year":"2004","unstructured":"Lange K (2004) Optimization. Springer, New York"},{"key":"350_CR13","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1007\/s11222-013-9379-3","volume":"24","author":"S Lee","year":"2014","unstructured":"Lee S, Huang JZ (2014) A biclustering algorithm for binary matrices based on penalized Bernoulli likelihood. Stat Comput 24:429\u2013441","journal-title":"Stat Comput"},{"key":"350_CR14","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1214\/10-AOAS327","volume":"4","author":"S Lee","year":"2010","unstructured":"Lee S, Huang JZ, Hu J (2010) Sparse logistic principal component analysis for binary data. Ann Appl Stat 4:1579\u20131601","journal-title":"Ann Appl Stat"},{"key":"350_CR15","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s10994-005-5316-9","volume":"62","author":"T Li","year":"2006","unstructured":"Li T (2006) A unified view on clustering binary data. Mach Learn 62:199\u2013215","journal-title":"Mach Learn"},{"key":"350_CR16","unstructured":"Lichman M (2013) UCI machine learning repository [ http:\/\/archive.ics.uci.edu\/ml ]. University of California, School of Information and Computer Science, Irvine"},{"key":"350_CR17","first-page":"1865","volume":"14","author":"W Pan","year":"2013","unstructured":"Pan W, Shen X, Liu B (2013) Cluster analysis: unsupervised learning via supervised learning with a non-convex penalty. J Mach Learn Res 14:1865\u20131889","journal-title":"J Mach Learn Res"},{"key":"350_CR18","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1214\/15-STS530","volume":"30","author":"NG Polson","year":"2015","unstructured":"Polson NG, Scott JG, Willard BT (2015) Proximal algorithms in statistics and machine learning. Stat Sci 30:559\u2013581","journal-title":"Stat Sci"},{"key":"350_CR19","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1080\/01621459.1971.10482356","volume":"66","author":"WM Rand","year":"1971","unstructured":"Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66:846\u2013850","journal-title":"J Am Stat Assoc"},{"key":"350_CR20","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1198\/jasa.2010.tm09380","volume":"105","author":"X Shen","year":"2010","unstructured":"Shen X, Huang HC (2010) Grouping pursuit through a regularization solution surface. J Am Stat Assoc 105:727\u2013739","journal-title":"J Am Stat Assoc"},{"key":"350_CR21","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1093\/biomet\/ass038","volume":"99","author":"X Shen","year":"2012","unstructured":"Shen X, Pan W (2012) Simultaneous supervised clustering and feature selection over a graph. Biometrika 99:899\u2013914","journal-title":"Biometrika"},{"key":"350_CR22","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1111\/1467-9868.00293","volume":"63","author":"R Tibshirani","year":"2001","unstructured":"Tibshirani R, Walther G, Hastie T (2001) Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc Ser B 63:411\u2013423","journal-title":"J R Stat Soc Ser B"},{"key":"350_CR23","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1198\/004017005000000139","volume":"47","author":"BA Turlach","year":"2005","unstructured":"Turlach BA, Venables W (2005) Simultaneous variable selection. Technometrics 47:349\u2013363","journal-title":"Technometrics"},{"key":"350_CR24","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1198\/jasa.2010.tm09415","volume":"105","author":"DM Witten","year":"2010","unstructured":"Witten DM, Tibshirani R (2010) A framework for feature selection in clustering. J Am Stat Assoc 105:713\u2013726","journal-title":"J Am Stat Assoc"},{"key":"350_CR25","first-page":"1","volume":"17","author":"C Wu","year":"2016","unstructured":"Wu C, Kwon S, Shen X, Pan W (2016) A new algorithm and theory for penalized regression-based clustering. J Mach Learn Res 17:1\u201325","journal-title":"J Mach Learn Res"},{"key":"350_CR26","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1089\/cmb.2017.0051","volume":"24","author":"H Yang","year":"2017","unstructured":"Yang H, Liu X (2017) Studies on the clustering algorithm for analyzing gene expression data with a bidirectional penalty. J Comput Biol 24:689\u2013698","journal-title":"J Comput Biol"},{"key":"350_CR27","doi-asserted-by":"crossref","unstructured":"Yang Y, Guan X, You J (2002) CLOPE: a fast and effective clustering algorithm for transactional data. In: SIGKDD \u201902 Edmonton, Alberta, Canada, pp 682\u2013687","DOI":"10.1145\/775047.775149"},{"key":"350_CR28","doi-asserted-by":"crossref","unstructured":"Zhang Z, Li T, Ding C, Zhang X (2007) Binary matrix factorization with applications. In: IEEE international conference on data mining, pp 391\u2013400","DOI":"10.1109\/ICDM.2007.99"}],"container-title":["Advances in Data Analysis and Classification"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11634-018-0350-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11634-018-0350-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11634-018-0350-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T22:52:39Z","timestamp":1662418359000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11634-018-0350-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,14]]},"references-count":28,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["350"],"URL":"https:\/\/doi.org\/10.1007\/s11634-018-0350-1","relation":{},"ISSN":["1862-5347","1862-5355"],"issn-type":[{"value":"1862-5347","type":"print"},{"value":"1862-5355","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,14]]},"assertion":[{"value":"27 February 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2018","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2018","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2018","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}