{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T22:58:04Z","timestamp":1773701884950,"version":"3.50.1"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031216855","type":"print"},{"value":"9783031216862","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21686-2_28","type":"book-chapter","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T08:30:15Z","timestamp":1668760215000},"page":"398-412","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Generating Diverse Clustering Datasets with\u00a0Targeted Characteristics"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3277-7557","authenticated-orcid":false,"given":"Luiz Henrique","family":"dos Santos Fernandes","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2718-7680","authenticated-orcid":false,"given":"Kate","family":"Smith-Miles","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6140-571X","authenticated-orcid":false,"given":"Ana Carolina","family":"Lorena","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,19]]},"reference":[{"key":"28_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1007\/978-3-030-92185-9_51","volume-title":"Neural Information Processing","author":"LHS Fernandes","year":"2021","unstructured":"Fernandes, L.H.S., de Souto, M.C.P., Lorena, A.C.: Evaluating data characterization measures for\u00a0clustering problems in\u00a0meta-learning. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds.) ICONIP 2021. LNCS, vol. 13108, pp. 621\u2013632. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-92185-9_51"},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Fernandes, L.H.d.S., Lorena, A.C., Smith-Miles, K.: Towards understanding clustering problems and algorithms: An instance space analysis. Algorithms 14(3), 95 (2021)","DOI":"10.3390\/a14030095"},{"key":"28_CR3","first-page":"20","volume":"2","author":"J Handl","year":"2005","unstructured":"Handl, J., Knowles, J.: Cluster generators for large high-dimensional data sets with large numbers of clusters. Dimension 2, 20 (2005)","journal-title":"Dimension"},{"issue":"3","key":"28_CR4","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/s00357-019-9312-3","volume":"36","author":"F Iglesias","year":"2019","unstructured":"Iglesias, F., Zseby, T., Ferreira, D., Zimek, A.: Mdcgen: Multidimensional dataset generator for clustering. J. Classification 36(3), 599\u2013618 (2019)","journal-title":"J. Classification"},{"issue":"2","key":"28_CR5","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/s10618-019-00661-z","volume":"34","author":"S Kandanaarachchi","year":"2020","unstructured":"Kandanaarachchi, S., Mu\u00f1oz, M.A., Hyndman, R.J., Smith-Miles, K.: On normalization and algorithm selection for unsupervised outlier detection. Data Mining Knowl. Disc. 34(2), 309\u2013354 (2020)","journal-title":"Data Mining Knowl. Disc."},{"issue":"2","key":"28_CR6","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.ijforecast.2016.09.004","volume":"33","author":"Y Kang","year":"2017","unstructured":"Kang, Y., Hyndman, R.J., Smith-Miles, K.: Visualising forecasting algorithm performance using time series instance spaces. Int. J. Forecast. 33(2), 345\u2013358 (2017)","journal-title":"Int. J. Forecast."},{"issue":"4","key":"28_CR7","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1207\/s15327906mbr2104_5","volume":"21","author":"GW Milligan","year":"1986","unstructured":"Milligan, G.W., Cooper, M.C.: A study of the comparability of external criteria for hierarchical cluster analysis. Multivariate Behav. Res. 21(4), 441\u2013458 (1986)","journal-title":"Multivariate Behav. Res."},{"issue":"4","key":"28_CR8","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1162\/evco_a_00194","volume":"25","author":"MA Mu\u00f1oz","year":"2017","unstructured":"Mu\u00f1oz, M.A., Smith-Miles, K.A.: Performance analysis of continuous black-box optimization algorithms via footprints in instance space. Evol. Comput. 25(4), 529\u2013554 (2017)","journal-title":"Evol. Comput."},{"issue":"1","key":"28_CR9","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/s10994-017-5629-5","volume":"107","author":"MA Munoz","year":"2018","unstructured":"Munoz, M.A., Villanova, L., Baatar, D., Smith-Miles, K.: Instance spaces for machine learning classification. Mach. Learn. 107(1), 109\u2013147 (2018)","journal-title":"Mach. Learn."},{"issue":"2","key":"28_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3436893","volume":"15","author":"MA Mu\u00f1oz","year":"2021","unstructured":"Mu\u00f1oz, M.A., et al.: An instance space analysis of regression problems. ACM Trans. Knowl. Discovery Data (TKDD) 15(2), 1\u201325 (2021)","journal-title":"ACM Trans. Knowl. Discovery Data (TKDD)"},{"key":"28_CR11","unstructured":"Pei, Y., Za\u00efane, O.: A synthetic data generator for clustering and outlier analysis. Tech. rep., Department of Computing Science, University of Alberta Edmonton, AB, Canada (2006). https:\/\/era.library.ualberta.ca\/items\/63beb6a7-cc50-4ffd-990b-64723b1e4bf9"},{"key":"28_CR12","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.ins.2018.10.043","volume":"477","author":"BA Pimentel","year":"2019","unstructured":"Pimentel, B.A., de Carvalho, A.C.: A new data characterization for selecting clustering algorithms using meta-learning. Inform. Sci. 477, 203\u2013219 (2019)","journal-title":"Inform. Sci."},{"issue":"2","key":"28_CR13","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.: Generation of random clusters with specified degree of separation. J. Classification 23(2), 315\u2013334 (2006)","journal-title":"J. Classification"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Rice, J.R.: The algorithm selection problem. In: Advances in Computers, vol. 15, pp. 65\u2013118. Elsevier (1976)","DOI":"10.1016\/S0065-2458(08)60520-3"},{"issue":"12","key":"28_CR15","doi-asserted-by":"publisher","first-page":"1976","DOI":"10.14778\/2824032.2824115","volume":"8","author":"E Schubert","year":"2015","unstructured":"Schubert, E., Koos, A., Emrich, T., Z\u00fcfle, A., Schmid, K.A., Zimek, A.: A framework for clustering uncertain data. Proc. VLDB Endowment 8(12), 1976\u20131979 (2015)","journal-title":"Proc. VLDB Endowment"},{"key":"28_CR16","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.cor.2013.11.015","volume":"45","author":"K Smith-Miles","year":"2014","unstructured":"Smith-Miles, K., Baatar, D., Wreford, B., Lewis, R.: Towards objective measures of algorithm performance across instance space. Comput. Op. Res. 45, 12\u201324 (2014)","journal-title":"Comput. Op. Res."},{"key":"28_CR17","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.cor.2015.04.022","volume":"63","author":"K Smith-Miles","year":"2015","unstructured":"Smith-Miles, K., Bowly, S.: Generating new test instances by evolving in instance space. Comput. Oper. Res. 63, 102\u2013113 (2015)","journal-title":"Comput. Oper. Res."},{"issue":"2","key":"28_CR18","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.: Oclus: an analytic method for generating clusters with known overlap. J. Classification 22(2), 221\u2013250 (2005)","journal-title":"J. Classification"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21686-2_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:12:48Z","timestamp":1709831568000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21686-2_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031216855","9783031216862"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21686-2_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Campinas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www2.sbc.org.br\/bracis2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"JEMS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"225","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"89","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}