{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T11:26:30Z","timestamp":1648725990492},"reference-count":15,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T00:00:00Z","timestamp":1569888000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T00:00:00Z","timestamp":1569888000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Classif"],"published-print":{"date-parts":[[2019,10]]},"DOI":"10.1007\/s00357-019-09356-y","type":"journal-article","created":{"date-parts":[[2019,12,3]],"date-time":"2019-12-03T12:03:25Z","timestamp":1575374605000},"page":"393-396","source":"Crossref","is-referenced-by-count":0,"title":["Editorial: Journal of Classification Vol. 36-3"],"prefix":"10.1007","volume":"36","author":[{"given":"Douglas L.","family":"Steinley","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,3]]},"reference":[{"key":"9356_CR1","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1007\/s00357-018-9282-x","volume":"36","author":"M Abaszade","year":"2019","unstructured":"Abaszade, M., & Effati, S. (2019). A new method for classifying random variables based on support vector machine. Journal of Classification, 36, 152\u2013174.","journal-title":"Journal of Classification"},{"key":"9356_CR2","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1080\/01621459.1958.10501479","volume":"53","author":"WD Fisher","year":"1958","unstructured":"Fisher, W. D. (1958). On grouping for maximum heterogeneity. Journal of the American Statistical Association, 53, 789\u2013798.","journal-title":"Journal of the American Statistical Association"},{"key":"9356_CR3","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s00357-018-9275-9","volume":"36","author":"A Flynt","year":"2019","unstructured":"Flynt, A., & Dean, N. (2019). Growth mixture modeling with measurement selection. Journal of Classification, 36, 3\u201325.","journal-title":"Journal of Classification"},{"key":"9356_CR4","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/s00357-012-9115-2","volume":"29","author":"S Herrmann","year":"2012","unstructured":"Herrmann, S., Hubert, K. T., Moulton, V., & Spillner, A. (2012). Recognizing treelike k-dissimilarities. Journal of Classification, 29, 321\u2013340.","journal-title":"Journal of Classification"},{"key":"9356_CR5","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/s00357-018-9255-0","volume":"35","author":"H-F K\u00f6hn","year":"2018","unstructured":"K\u00f6hn, H.-F., & Chiu, C.-Y. (2018). How to build a complete Q-matrix for a cognitively diagnostic test. Journal of Classification, 35, 273\u2013299.","journal-title":"Journal of Classification"},{"key":"9356_CR6","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF02294153","volume":"50","author":"GW Milligan","year":"1985","unstructured":"Milligan, G. W. (1985). An algorithm for generating artificial test clusters. Psychometrika, 50, 123\u2013127.","journal-title":"Psychometrika"},{"key":"9356_CR7","doi-asserted-by":"publisher","first-page":"8577","DOI":"10.1073\/pnas.0601602103","volume":"103","author":"MEJ Newman","year":"2006","unstructured":"Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Science, 103, 8577\u20138582.","journal-title":"Proceedings of the National Academy of Science"},{"key":"9356_CR8","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1007\/s00357-017-9242-x","volume":"34","author":"H Nunez","year":"2017","unstructured":"Nunez, H., Gonzalez-Abril, L., & Angulo, C. (2017). Improving SVM classification on imbalanced datasets by introducing a new bias. Journal of Classification, 34, 427\u2013443.","journal-title":"Journal of Classification"},{"key":"9356_CR9","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/s00357-006-0018-y","volume":"23","author":"W Qiu","year":"2006","unstructured":"Qiu, W., & Joe, H. (2006). Generation of random clusters with specified degree of separation. Journal of Classification, 23, 315\u2013334.","journal-title":"Journal of Classification"},{"key":"9356_CR10","first-page":"1976","volume":"8","author":"E Schubert","year":"2015","unstructured":"Schubert, E., Koos, A., Emrich, T., Zufle, A., Schmid, K. A., & Zimek, A. (2015). A framework for clustering uncertain data. PVLDB, 8, 1976\u20131979.","journal-title":"PVLDB"},{"key":"9356_CR11","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1037\/1082-989X.8.3.294","volume":"8","author":"D Steinley","year":"2003","unstructured":"Steinley, D. (2003). Local optima in K-means clustering: What you don\u2019t know may hurt you. Psychological Methods, 8, 294\u2013304.","journal-title":"Psychological Methods"},{"key":"9356_CR12","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1037\/1082-989X.11.2.178","volume":"11","author":"D Steinley","year":"2006","unstructured":"Steinley, D. (2006). Profiling local optima in K-means clustering: Developing a diagnostic technique. Psychological Methods, 11, 178\u2013192.","journal-title":"Psychological Methods"},{"key":"9356_CR13","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s00357-005-0015-6","volume":"22","author":"D Steinley","year":"2005","unstructured":"Steinley, D., & Henson, R. (2005). OCLUS: An analytic method for generating clusters with known overlap. Journal of Classification, 22, 221\u2013250.","journal-title":"Journal of Classification"},{"key":"9356_CR14","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s11336-008-9058-z","volume":"73","author":"D Steinley","year":"2008","unstructured":"Steinley, D., & Hubert, L. (2008). Order constrained solutions in K-means clustering: Even better than being globally optimal. Psychometrika, 73, 647\u2013664.","journal-title":"Psychometrika"},{"key":"9356_CR15","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/s00357-018-9249-y","volume":"35","author":"W Zhu","year":"2018","unstructured":"Zhu, W., Song, Y., & Xiao, Y. (2018). A new support vector machine plus with pinball loss. Journal of Classification, 35, 52\u201370.","journal-title":"Journal of Classification"}],"container-title":["Journal of Classification"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00357-019-09356-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00357-019-09356-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00357-019-09356-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T19:13:51Z","timestamp":1606850031000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00357-019-09356-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10]]},"references-count":15,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,10]]}},"alternative-id":["9356"],"URL":"https:\/\/doi.org\/10.1007\/s00357-019-09356-y","relation":{},"ISSN":["0176-4268","1432-1343"],"issn-type":[{"value":"0176-4268","type":"print"},{"value":"1432-1343","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10]]}}}