{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T19:57:41Z","timestamp":1743105461785,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030587987"},{"type":"electronic","value":"9783030587994"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58799-4_73","type":"book-chapter","created":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T13:07:30Z","timestamp":1601471250000},"page":"1017-1031","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving the Clustering Algorithms Automatic Generation Process with Cluster Quality Indexes"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3634-1555","authenticated-orcid":false,"given":"Michel","family":"Montenegro","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1222-5269","authenticated-orcid":false,"given":"Aruanda","family":"Meiguins","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5872-4827","authenticated-orcid":false,"given":"Bianchi","family":"Meiguins","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8566-3238","authenticated-orcid":false,"given":"Jefferson","family":"Morais","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,1]]},"reference":[{"key":"73_CR1","unstructured":"Han, J., Kamber, M., Pei, J.: Data Mining Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers (2012). http:\/\/myweb.sabanciuniv.edu\/rdehkharghani\/files\/2016\/02\/The-Morgan-Kaufmann-Series-in-Data-Management-Systems-Jiawei-Han-Micheline-Kamber-Jian-Pei-Data-Mining.-Concepts-and-Techniques-3rd-Edition-Morgan-Kaufmann-2011.pdf. Accessed 17 Apr 2020"},{"key":"73_CR2","doi-asserted-by":"publisher","unstructured":"Matake, N., Hiroyasu, T., Miki, M., Senda, T.: Multiobjective clustering with automatic k-determination for large-scale data. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 861\u2013868 (2007) https:\/\/doi.org\/10.1145\/1276958.1277126","DOI":"10.1145\/1276958.1277126"},{"key":"73_CR3","doi-asserted-by":"publisher","unstructured":"Faceli, K., Souto, M.C.P., Ara\u00fajo, D.S.A., Carvalho, A.C.P.L.F.: Multi-objective clustering ensemble for gene expression data analysis. J. Neurocomput. 72(13\u201315), 2763\u20132774 (2009). https:\/\/doi.org\/10.1016\/j.neucom.2008.09.025","DOI":"10.1016\/j.neucom.2008.09.025"},{"key":"73_CR4","doi-asserted-by":"publisher","first-page":"105971","DOI":"10.1016\/j.asoc.2019.105971","volume":"87","author":"V Antunes","year":"2020","unstructured":"Antunes, V., Sakata, T.C., Faceli, K., Souto, M.: Hybrid strategy for selecting compact set of clustering partitions. Appl. Soft Comput. 87, 105971 (2020). https:\/\/doi.org\/10.1016\/j.asoc.2019.105971","journal-title":"Appl. Soft Comput."},{"key":"73_CR5","doi-asserted-by":"crossref","unstructured":"Meiguins, A.S.G., Limao, R.C., Meiguins, B.S., Junior, S.F.S., Freitas, A.A.: AutoClustering: an estimation of distribution algorithm for the automatic generation of clustering algorithms. In: Proceedings of WCCI 2012 - IEEE World Congress on Computational Intelligence (Congress on Evolutionary Computation), pp. 2560\u20132566. IEEE Press (2012)","DOI":"10.1109\/CEC.2012.6252874"},{"key":"73_CR6","doi-asserted-by":"publisher","unstructured":"Dudoit, S., Fridlyand, J.: A prediction-based resampling method for estimating the number of clusters in a data set. Genome Biol. 3(7), research0036.1 (2002) https:\/\/doi.org\/10.1186\/gb-2002-3-7-research0036","DOI":"10.1186\/gb-2002-3-7-research0036"},{"key":"73_CR7","volume-title":"Data Mining: Practical Machine Learning Tools and Techniques","author":"IH Witten","year":"2016","unstructured":"Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)"},{"issue":"3","key":"73_CR8","doi-asserted-by":"publisher","first-page":"88","DOI":"10.3390\/info10030088","volume":"10","author":"A Meiguins","year":"2019","unstructured":"Meiguins, A., Santos, Y., Santos, D., Meiguins, B., Morais, J.: Visual analysis scenarios for understanding evolutionary computational techniques\u2019 behavior. Inf. Open Access J. 10(3), 88 (2019). https:\/\/doi.org\/10.3390\/info10030088","journal-title":"Inf. Open Access J."},{"issue":"1","key":"73_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","volume":"14","author":"X Wu","year":"2007","unstructured":"Wu, X., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1\u201337 (2007). https:\/\/doi.org\/10.1007\/s10115-007-0114-2","journal-title":"Knowl. Inf. Syst."},{"key":"73_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","volume":"33","author":"L Rokach","year":"2010","unstructured":"Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33, 1\u201339 (2010). https:\/\/doi.org\/10.1007\/s10462-009-9124-7","journal-title":"Artif. Intell. Rev."},{"key":"73_CR11","unstructured":"Rendon, E., Abundez, I., Arizmendi, A., Quiroz, E.M.: Internal versus external cluster validation indexes. Int. J. Comput. Commun. 5(1), 27\u201334 (2011). https:\/\/www.researchgate.net\/profile\/Erendira_Rendon. Accessed 17 Apr 2020"},{"key":"73_CR12","unstructured":"Rendon, E., Abundez, I., Gutierrez, C., D\u00e1az, S.: A comparison of internal and external cluster validation indexes. In: Proceedings of the 2011 American Conference, San Francisco, CA, USA (2011). https:\/\/www.researchgate.net\/profile\/Erendira_Rendon. Accessed 17 Apr 2020"},{"key":"73_CR13","doi-asserted-by":"publisher","unstructured":"Sanchez, M.S., Valdovinos, R.M., Trueba, A., Rendon, E., Alejo, R., Lopez, E.: Applicability of cluster validation indexes for large data sets. In: 12th Mexican International Conference on Artificial Intelligence (2013). https:\/\/doi.org\/10.1109\/micai.2013.30","DOI":"10.1109\/micai.2013.30"},{"key":"73_CR14","unstructured":"Lichman, M.: UCI Machine Learning Repository. University of California, Oakland, CA, USA (2013)"},{"key":"73_CR15","unstructured":"Spiehler, V.: From USA Forensic Science Service; 6 types of glass; defined in terms of their oxide content (i.e. Na, Fe, K, etc). http:\/\/archive.ics.uci.edu\/ml\/datasets\/glass+identification. Accessed 25 Apr 2020"},{"key":"73_CR16","unstructured":"Aha, D.W.: 4 databases: Cleveland, Hungary, Switzerland, and the VA Long Beach. http:\/\/archive.ics.uci.edu\/ml\/datasets\/Heart+Disease. Accessed 25 Apr 2020"},{"key":"73_CR17","unstructured":"Forsyth R.S.: BUPA Medical Research Ltd. http:\/\/archive.ics.uci.edu\/ml\/datasets\/liver+disorders. Accessed 25 Apr 2020"},{"key":"73_CR18","unstructured":"Brito, Y.P.S., Santos, C.G.R., Mendon\u00e7a, S.P., Ar\u00e1ujo, T.D., Freitas, A.A., Meiguins, B.S.: A prototype application to generate synthetic datasets for information visualization evaluations. In: 2018 22nd International Conference Information Visualisation (IV), pp. 153\u2013158 (2018)"},{"key":"73_CR19","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s13748-011-0005-3","volume":"1","author":"J Ceberio","year":"2012","unstructured":"Ceberio, J., Irurozki, E., Mendiburu, A., et al.: A review on estimation of distribution algorithms in permutation-based combinatorial optimization problems. Prog. Artif. Intell. 1, 103\u2013117 (2012). https:\/\/doi.org\/10.1007\/s13748-011-0005-3","journal-title":"Prog. Artif. Intell."},{"issue":"4","key":"73_CR20","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1016\/s0165-1684(02)00475-9","volume":"83","author":"N Bolshakova","year":"2003","unstructured":"Bolshakova, N., Azuaje, F.: Cluster validation techniques for genome expression data. Sig. Process. 83(4), 825\u2013833 (2003). https:\/\/doi.org\/10.1016\/s0165-1684(02)00475-9","journal-title":"Sig. Process."},{"issue":"6","key":"73_CR21","doi-asserted-by":"publisher","first-page":"540","DOI":"10.4097\/kjae.2015.68.6.540","volume":"68","author":"TK Kim","year":"2015","unstructured":"Kim, T.K.: T test as a parametric statistic. Korean J. Anesthesiol. 68(6), 540 (2015). https:\/\/doi.org\/10.4097\/kjae.2015.68.6.540","journal-title":"Korean J. Anesthesiol."},{"key":"73_CR22","doi-asserted-by":"publisher","unstructured":"Liu, Y., Li, Z., Xiong, H., Gao, X., Wu, J.: Understanding of internal clustering validation measures. In: IEEE International Conference on Data Mining (2010). https:\/\/doi.org\/10.1109\/icdm.2010.35","DOI":"10.1109\/icdm.2010.35"},{"key":"73_CR23","unstructured":"Tan, P.N., Kumar, V., Steinbach, M.: Introduction to Data Mining, 1st edn. Pearson Addison Wesley (2006). http:\/\/repository.fue.edu.eg\/xmlui\/bitstream\/handle\/123456789\/3583\/8857.pdf. Accessed 12 Apr 2020"},{"key":"73_CR24","doi-asserted-by":"publisher","unstructured":"Meiguins, A.S.G., Freitas, A.A., Lim\u00e3o, R.C., Junior, S.F.S., Meiguins, B.S.: An estimation of distribution algorithm for the automatic generation of clustering algorithms. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation - GECCO 2010 (2010). https:\/\/doi.org\/10.1145\/1830483.1830679","DOI":"10.1145\/1830483.1830679"},{"key":"73_CR25","unstructured":"Frank, A., Asuncion, A.: UCI Machine Learning Repository. Journal Irvine, University of California, School of Information and Computer Science, CA (2010). http:\/\/archive.ics.uci.edu\/ml. Accessed 05 Apr 2020"},{"issue":"7","key":"73_CR26","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1136\/archdischild-2014-307149","volume":"100","author":"TJ Cole","year":"2015","unstructured":"Cole, T.J.: Too many digits: the presentation of numerical data: Table 1. Arch. Dis. Child. 100(7), 608\u2013609 (2015). https:\/\/doi.org\/10.1136\/archdischild-2014-307149","journal-title":"Arch. Dis. Child."},{"key":"73_CR27","doi-asserted-by":"publisher","unstructured":"Tiwari, R., Singh, M.: Correlation-based attribute selection using genetic algorithm. Int. J. Comput. Appl. 4(8) (2010). https:\/\/doi.org\/10.5120\/847-1182","DOI":"10.5120\/847-1182"},{"key":"73_CR28","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53\u201365 (1987). https:\/\/doi.org\/10.1016\/0377-0427(87)90125-7","journal-title":"J. Comput. Appl. Math."},{"issue":"1","key":"73_CR29","first-page":"72","volume":"2","author":"K Ahuja","year":"2013","unstructured":"Ahuja, K., Saini, A.: Analyzing formation of K mean clusters using similarity and dissimilarity measures. Int. J. Adv. Trends Comput. Sci. Eng. - IJATCSE 2(1), 72\u201374 (2013)","journal-title":"Int. J. Adv. Trends Comput. Sci. Eng. - IJATCSE"},{"issue":"2","key":"73_CR30","doi-asserted-by":"publisher","first-page":"e56152","DOI":"10.1371\/journal.pone.0056152","volume":"8","author":"R Bhm","year":"2013","unstructured":"Bhm, R., Rockenbach, B.: The inter-group comparison - intra-group cooperation hypothesis: comparisons between groups increase efficiency in public goods provision. PLoS ONE 8(2), e56152 (2013). https:\/\/doi.org\/10.1371\/journal.pone.0056152","journal-title":"PLoS ONE"},{"key":"73_CR31","doi-asserted-by":"publisher","unstructured":"Patro, S.G., Sahu, K.K.: Normalization: a preprocessing stage. Int. Adv. Res. J. Sci. Eng. Technol. - IARJSET 2(3) (2015). https:\/\/doi.org\/10.17148\/IARJSET.2015.2305","DOI":"10.17148\/IARJSET.2015.2305"},{"key":"73_CR32","doi-asserted-by":"publisher","first-page":"e4483","DOI":"10.1136\/bmj.e4483","volume":"345","author":"P Sedgwick","year":"2012","unstructured":"Sedgwick, P.: Pearson\u2019s correlation coefficient. BMJ 345, e4483\u2013e4483 (2012). https:\/\/doi.org\/10.1136\/bmj.e4483","journal-title":"BMJ"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58799-4_73","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T16:36:48Z","timestamp":1619195808000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58799-4_73"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030587987","9783030587994"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58799-4_73","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"1 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cagliari","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iccsa.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Cyber chair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1450","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":"466","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":"32","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":"32% - 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":"2.5","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":"6","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)"}},{"value":"Conference was held virtually due to COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}