{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:16:55Z","timestamp":1743099415596,"version":"3.40.3"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030917012"},{"type":"electronic","value":"9783030917029"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-91702-9_14","type":"book-chapter","created":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T20:02:46Z","timestamp":1638043366000},"page":"202-217","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving a Genetic Clustering Approach with a CVI-Based Objective Function"],"prefix":"10.1007","author":[{"given":"Caio","family":"Flexa","sequence":"first","affiliation":[]},{"given":"Walisson","family":"Gomes","sequence":"additional","affiliation":[]},{"given":"Igor","family":"Moreira","sequence":"additional","affiliation":[]},{"given":"Reginaldo","family":"Santos","sequence":"additional","affiliation":[]},{"given":"Claudomiro","family":"Sales","sequence":"additional","affiliation":[]},{"given":"Mois\u00e9s","family":"Silva","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,28]]},"reference":[{"key":"14_CR1","first-page":"170","volume":"91","author":"M Alswaitti","year":"2018","unstructured":"Alswaitti, M., Albughdadi, M., Isa, N.A.M.: Density-based particle swarm optimization algorithm for data clustering. ESWA 91, 170\u2013186 (2018)","journal-title":"ESWA"},{"key":"14_CR2","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.eswa.2016.02.009","volume":"55","author":"G Armano","year":"2016","unstructured":"Armano, G., Farmani, M.R.: Multiobjective clustering analysis using particle swarm optimization. Expert Syst. Appl. 55, 184\u2013193 (2016)","journal-title":"Expert Syst. Appl."},{"key":"14_CR3","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.eswa.2017.05.064","volume":"86","author":"AE Bay\u00e1","year":"2017","unstructured":"Bay\u00e1, A.E., Larese, M.G., Nam\u00edas, R.: Clustering stability for automated color image segmentation. Expert Syst. Appl. 86, 258\u2013273 (2017)","journal-title":"Expert Syst. Appl."},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Bezdek, J.C., Pal, N.R.: Some new indexes of cluster validity. TSMC-B (1998)","DOI":"10.1109\/3477.678624"},{"issue":"9","key":"14_CR5","doi-asserted-by":"publisher","first-page":"966","DOI":"10.1016\/j.patrec.2010.01.002","volume":"31","author":"R Campello","year":"2010","unstructured":"Campello, R.: Generalized external indexes for comparing data partitions with overlapping categories. Pattern Recogn. Lett. 31(9), 966\u2013975 (2010)","journal-title":"Pattern Recogn. Lett."},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Cremona, C.: Big data and structural health monitoring. In: Challenges in Design and Construction of an Innovative and Sustainable Built Environment, 19th IABSE Congress Stockholm, pp. 1793\u20131801, September 2016","DOI":"10.2749\/stockholm.2016.1793"},{"key":"14_CR7","unstructured":"Daniel, W.W.: Applied nonparametric statistics. PWS-KENT, USA (1990)"},{"issue":"2","key":"14_CR8","first-page":"182","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-ii. IEEE TEC 6(2), 182\u2013197 (2002)","journal-title":"IEEE TEC"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Diez, A., Khoa, N.L.D., Makki Alamdari, M., Wang, Y., Chen, F., Runcie, P.: A clustering approach for structural health monitoring on bridges. JCSHM (2016)","DOI":"10.1007\/s13349-016-0160-0"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Dziopa, T.: Clustering validity indices evaluation with regard to semantic homogeneity. In: FedCSIS 2016, Gda\u0144sk, Poland, 11\u201314 September 2016, pp. 3\u20139 (2016)","DOI":"10.15439\/2016F371"},{"issue":"6","key":"14_CR11","first-page":"1194","volume":"25","author":"N Esfandian","year":"2012","unstructured":"Esfandian, N., Razzazi, F., Behrad, A.: A clustering based feature selection method in spectro-temporal domain for speech recognition. EAAI 25(6), 1194\u20131202 (2012)","journal-title":"EAAI"},{"key":"14_CR12","first-page":"225","volume":"128","author":"C Flexa","year":"2019","unstructured":"Flexa, C., Santos, R., Gomes, W., Sales, C., Costa, J.C.: Mutual equidistant-scattering criterion: a new index for crisp clustering. ESWA 128, 225\u2013245 (2019)","journal-title":"ESWA"},{"issue":"12","key":"14_CR13","doi-asserted-by":"publisher","first-page":"4743","DOI":"10.1007\/s10489-018-1238-7","volume":"48","author":"P Fr\u00e4nti","year":"2018","unstructured":"Fr\u00e4nti, P., Sieranoja, S.: K-means properties on six clustering benchmark datasets. Appl. Intell. 48(12), 4743\u20134759 (2018)","journal-title":"Appl. Intell."},{"key":"14_CR14","first-page":"437","volume":"53","author":"E Fumeo","year":"2015","unstructured":"Fumeo, E., Oneto, L., Anguita, D.: Condition based maintenance in railway transportation systems based on big data streaming analysis. PCS 53, 437\u2013446 (2015)","journal-title":"PCS"},{"issue":"2","key":"14_CR15","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.ijinfomgt.2014.10.007","volume":"35","author":"A Gandomi","year":"2015","unstructured":"Gandomi, A., Haider, M.: Beyond the hype: big data concepts, methods, and analytics. Int. J. Inf. Manage. 35(2), 137\u2013144 (2015)","journal-title":"Int. J. Inf. Manage."},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Garc\u00eda, S., Fern\u00e1ndez, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. IS 180(10), 2044\u20132064 (2010)","DOI":"10.1016\/j.ins.2009.12.010"},{"issue":"4","key":"14_CR17","first-page":"374","volume":"58","author":"A Gardiner","year":"2018","unstructured":"Gardiner, A., Aasheim, C., Rutner, P., Williams, S.: Skill requirements in big data: a content analysis of job advertisements. JCIF 58(4), 374\u2013384 (2018)","journal-title":"JCIF"},{"key":"14_CR18","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.future.2015.07.019","volume":"63","author":"D Gil","year":"2016","unstructured":"Gil, D., Songi, I.Y.: Modeling and management of big data: challenges and opportunities. Futur. Gener. Comput. Syst. 63, 96\u201399 (2016)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"14_CR19","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.eswa.2016.10.022","volume":"69","author":"E G\u00fcng\u00f6r","year":"2017","unstructured":"G\u00fcng\u00f6r, E., \u00d6zmen, A.: Distance and density based clustering algorithm using gaussian Kernel. Expert Syst. Appl. 69, 10\u201320 (2017)","journal-title":"Expert Syst. Appl."},{"key":"14_CR20","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1016\/j.eswa.2017.09.005","volume":"91","author":"MZ Islam","year":"2018","unstructured":"Islam, M.Z., Estivill-Castro, V., Rahman, M.A., Bossomaier, T.: Combining k-means and a genetic algorithm through a novel arrangement of genetic operators for high quality clustering. Expert Syst. Appl. 91, 402\u2013417 (2018)","journal-title":"Expert Syst. Appl."},{"issue":"8","key":"14_CR21","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","volume":"31","author":"AK Jain","year":"2010","unstructured":"Jain, A.K.: Data clustering: 50 years beyond k-means. PRL 31(8), 651\u2013666 (2010)","journal-title":"PRL"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Johnson, S.C.: Hierarchical clustering schemes. Psychometrika, pp. 241\u2013254 (1967)","DOI":"10.1007\/BF02289588"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Langone, R., Reynders, E., Mehrkanoon, S., Suykens, J.A.: Automated structural health monitoring based on adaptive kernel spectral clustering. In: MSSP (2017)","DOI":"10.1016\/j.ymssp.2016.12.002"},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Lingras, P., Chen, M., Miao, D.: Qualitative and quantitative combinations of crisp and rough clustering schemes using dominance relations. In: IJAR, pp. 238\u2013258 (2014)","DOI":"10.1016\/j.ijar.2013.05.007"},{"issue":"3","key":"14_CR25","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1016\/0003-2670(93)80130-D","volume":"282","author":"C Lucasius","year":"1993","unstructured":"Lucasius, C., Dane, A., Kateman, G.: On k-medoid clustering of large data sets with the aid of a genetic algorithm: background, feasiblity and comparison. Anal. Chim. Acta 282(3), 647\u2013669 (1993)","journal-title":"Anal. Chim. Acta"},{"key":"14_CR26","unstructured":"MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: BSMSP, vol. 1, pp. 281\u2013297. University of California Press (1967)"},{"issue":"9","key":"14_CR27","first-page":"4009","volume":"10","author":"SAL Mary","year":"2015","unstructured":"Mary, S.A.L., Sivagami, A.N., Rani, M.U.: Cluster validity measures dynamic clustering algorithms. ARPN J. Eng. Appl. Sci. 10(9), 4009\u20134012 (2015)","journal-title":"ARPN J. Eng. Appl. Sci."},{"issue":"9","key":"14_CR28","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1016\/S0031-3203(99)00137-5","volume":"33","author":"U Maulik","year":"2000","unstructured":"Maulik, U., Bandyopadhyay, S.: Genetic algorithm-based clustering technique. Pattern Recogn. 33(9), 1455\u20131465 (2000)","journal-title":"Pattern Recogn."},{"key":"14_CR29","unstructured":"McAfee, A., Brynjolfsson, E.: Big data: the management revolution (2012)"},{"key":"14_CR30","doi-asserted-by":"crossref","unstructured":"Mclachlan, G., Basford, K.: Mixture Models: Inference and Applications to Clustering, vol. 38, January 1988","DOI":"10.2307\/2348072"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Moulavi, D., Jaskowiak, P.A., Campello, R.J.G.B., Zimek, A., Sander, J.: Density-based clustering validation. In: 14th SIAM ICDM, Philadelphia, PA (2014)","DOI":"10.1137\/1.9781611973440.96"},{"issue":"5","key":"14_CR32","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1016\/j.ygeno.2017.06.009","volume":"109","author":"IA Pagnuco","year":"2017","unstructured":"Pagnuco, I.A., Pastore, J.I., Abras, G., Brun, M., Ballarin, V.L.: Analysis of genetic association using hierarchical clustering and cluster validation indices. Genomics 109(5), 438\u2013445 (2017)","journal-title":"Genomics"},{"key":"14_CR33","first-page":"7094046","volume":"2017","author":"E Rubio","year":"2017","unstructured":"Rubio, E., Castillo, O., Valdez, F., Melin, P., Gonzalez, C.I., Martinez, G.: An extension of the fuzzy possibilistic clustering algorithm using type-2 fuzzy logic techniques. Adv. Fuzzy Sys. 2017, 7094046 (2017)","journal-title":"Adv. Fuzzy Sys."},{"key":"14_CR34","unstructured":"Salvador, S., Chan, P.: Determining the number of clusters\/segments in hierarchical clustering\/segmentation algorithms. In: 16th IEEE ICTAI, USA (2004)"},{"key":"14_CR35","first-page":"228","volume":"67","author":"JA Silva","year":"2017","unstructured":"Silva, J.A., Hruschka, E.R., Gama, J.: An evolutionary algorithm for clustering data streams with a variable number of clusters. ESWA 67, 228\u2013238 (2017)","journal-title":"ESWA"},{"key":"14_CR36","doi-asserted-by":"crossref","unstructured":"Silva, M., Santos, A., Figueiredo, E., Santos, R., Sales, C., Costa, J.C.W.A.: A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges. Eng. Appl. Artif. Intell. 52(C), 168\u2013180 (2016)","DOI":"10.1016\/j.engappai.2016.03.002"},{"key":"14_CR37","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.ymssp.2017.01.024","volume":"92","author":"M Silva","year":"2017","unstructured":"Silva, M., Santos, A., Santos, R., Figueiredo, E., Sales, C., Costa, J.C.: Agglomerative concentric hypersphere clustering applied to structural damage detection. Mech. Syst. Signal Process. 92, 196\u2013212 (2017)","journal-title":"Mech. Syst. Signal Process."},{"key":"14_CR38","unstructured":"Ultsch, A.: Clustering with SOM: U*C. In: Proceedings of Workshop on Self-organizing Maps, pp. 75\u201382, January 2005"},{"key":"14_CR39","doi-asserted-by":"publisher","first-page":"2267","DOI":"10.1016\/S0031-3203(01)00197-2","volume":"35","author":"KL Wu","year":"2002","unstructured":"Wu, K.L., Yang, M.S.: Alternative c-means clustering algorithms. Pattern Recogn. 35, 2267\u20132278 (2002)","journal-title":"Pattern Recogn."},{"key":"14_CR40","doi-asserted-by":"crossref","unstructured":"Wu, K.L., Yang, M.S.: A cluster validity index for fuzzy clustering. PRL (2005)","DOI":"10.1016\/j.patrec.2004.11.022"},{"issue":"3","key":"14_CR41","first-page":"645","volume":"16","author":"R Xu","year":"2005","unstructured":"Xu, R., Wunsch, D., II.: Survey of clustering algorithms. TNN 16(3), 645\u2013678 (2005)","journal-title":"TNN"},{"key":"14_CR42","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.asoc.2015.01.031","volume":"30","author":"CL Yang","year":"2015","unstructured":"Yang, C.L., Kuo, R., Chien, C.H., Quyen, N.T.P.: Non-dominated sorting genetic algorithm using fuzzy membership chromosome for categorical data clustering. Appl. Soft Comput. 30, 113\u2013122 (2015)","journal-title":"Appl. Soft Comput."},{"key":"14_CR43","unstructured":"Zhao, Q.: Cluster Validity in Clustering Methods. Ph.D. thesis, UEF, June 2012"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-91702-9_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T04:35:16Z","timestamp":1726202116000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-91702-9_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030917012","9783030917029"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-91702-9_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"28 November 2021","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/c4ai.inova.usp.br\/bracis\/","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":"192","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":"77","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":"3.1","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to COVID-19, the conference was held as an online event.","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)"}}]}}