{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:21:37Z","timestamp":1743092497501,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030192228"},{"type":"electronic","value":"9783030192235"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-19223-5_3","type":"book-chapter","created":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T23:22:43Z","timestamp":1558048963000},"page":"31-41","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["RETRACTED CHAPTER: U-Control Chart Based Differential Evolution Clustering for Determining the Number of Cluster in k-Means"],"prefix":"10.1007","author":[{"given":"Jes\u00fas","family":"Silva","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omar Bonerge Pineda","family":"Lezama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Noel","family":"Varela","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jes\u00fas Garc\u00eda","family":"Guiliany","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ernesto Steffens","family":"Sanabria","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Madelin S\u00e1nchez","family":"Otero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vladimir \u00c1lvarez","family":"Rojas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1016\/j.compeleceng.2018.04.023","volume":"68","author":"SB Salem","year":"2018","unstructured":"Salem, S.B., Naouali, S., Chtourou, Z.: A fast and effective partitional clustering algorithm for large categorical datasets using a k-means based approach. Comput. Electr. Eng. 68, 463\u2013483 (2018). https:\/\/doi.org\/10.1016\/j.compeleceng.2018.04.023","journal-title":"Comput. Electr. Eng."},{"key":"3_CR2","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.spl.2018.01.015","volume":"137","author":"S Chakraborty","year":"2018","unstructured":"Chakraborty, S., Das, S.: Simultaneous variable weighting and determining the number of clusters\u2014a weighted Gaussian means algorithm. Stat. Probab. Lett. 137, 148\u2013156 (2018). https:\/\/doi.org\/10.1016\/j.spl.2018.01.015","journal-title":"Stat. Probab. Lett."},{"key":"3_CR3","doi-asserted-by":"publisher","unstructured":"Masud, M.A, Huang, J.Z., Wei, C., Wang, J., Khan, I., Zhong, M.: I-nice: a new approach for identifying the number of clusters and initial cluster centres. Inf. Sci. (NY) (2018). https:\/\/doi.org\/10.1016\/j.ins.2018.07.034","DOI":"10.1016\/j.ins.2018.07.034"},{"key":"3_CR4","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.jksuci.2014.04.002","volume":"27","author":"MA Rahman","year":"2015","unstructured":"Rahman, M.A., Islam, M.Z., Bossomaier, T.: ModEx and seed-detective: two novel techniques for high quality clustering by using good initial seeds in k-means. J. King Saud Univ. \u2013 Comput. Inf. Sci. 27, 113\u2013128 (2015). https:\/\/doi.org\/10.1016\/j.jksuci.2014.04.002","journal-title":"J. King Saud Univ. \u2013 Comput. Inf. Sci."},{"key":"3_CR5","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.knosys.2014.08.011","volume":"71","author":"MA Rahman","year":"2014","unstructured":"Rahman, M.A., Islam, M.Z.: A hybrid clustering technique combining a novel genetic algorithm with k-means. Knowl.-Based Syst. 71, 345\u2013365 (2014). https:\/\/doi.org\/10.1016\/j.knosys.2014.08.011","journal-title":"Knowl.-Based Syst."},{"key":"3_CR6","doi-asserted-by":"publisher","unstructured":"Ramadas, M., Abraham, A., Kumar, S.: FSDE-forced strategy differential evolution used for data clustering. J. King Saud Univ. - Comput. Inf. Sci. (2016). https:\/\/doi.org\/10.1016\/j.jksuci.2016.12.005","DOI":"10.1016\/j.jksuci.2016.12.005"},{"key":"3_CR7","doi-asserted-by":"publisher","unstructured":"Yaqian, Z., Chai, Q.H., Boon, G.W.: Curvature-based method for determining the number of clusters. Inf. Sci. (NY) (2017). https:\/\/doi.org\/10.1016\/j.ins.2017.05.024","DOI":"10.1016\/j.ins.2017.05.024"},{"key":"3_CR8","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.ins.2018.02.001","volume":"439\u2013440","author":"C T\u00eern\u0103uc\u0103","year":"2018","unstructured":"T\u00eern\u0103uc\u0103, C., G\u00f3mez-P\u00e9rez, D., Balc\u00e1zar, J.L., Monta\u00f1a, J.L.: Global optimality in k-means clustering. Inf. Sci. (NY) 439\u2013440, 79\u201394 (2018). https:\/\/doi.org\/10.1016\/j.ins.2018.02.001","journal-title":"Inf. Sci. (NY)"},{"key":"3_CR9","doi-asserted-by":"publisher","unstructured":"Xiang, W., Zhu, N., Ma, S., Meng, X., An, M.: A dynamic shuffled differential evolution algorithm for data clustering. Neurocomputing (2015). https:\/\/doi.org\/10.1016\/j.neucom.2015.01.058","DOI":"10.1016\/j.neucom.2015.01.058"},{"key":"3_CR10","doi-asserted-by":"publisher","unstructured":"Garcia, A.J., Flores, W.G.: Automatic clustering using nature-inspired metaheuristics: a survey. Appl. Soft Comput. (2016). https:\/\/doi.org\/10.1016\/j.asoc.2015.12.001","DOI":"10.1016\/j.asoc.2015.12.001"},{"key":"3_CR11","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1109\/TSMCA.2007.909595","volume":"38","author":"S Das","year":"2008","unstructured":"Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Syst. Man Cybern. - Part A Syst. Hum. 38, 218\u2013237 (2008). https:\/\/doi.org\/10.1109\/TSMCA.2007.909595","journal-title":"IEEE Trans. Syst. Man Cybern. - Part A Syst. Hum."},{"key":"3_CR12","doi-asserted-by":"publisher","unstructured":"Omran, M.G.H., Engelbrecht, A.P., Salman, A.: Dynamic clustering using particle swarm optimization with application in image segmentation. Pattern Anal. Appl. 332\u2013344 (2006). https:\/\/doi.org\/10.1007\/s10044-005-0015-5","DOI":"10.1007\/s10044-005-0015-5"},{"key":"3_CR13","doi-asserted-by":"publisher","first-page":"1197","DOI":"10.1016\/S0031-3203(01)00108-X","volume":"35","author":"S Bandyopadhyay","year":"2002","unstructured":"Bandyopadhyay, S., Maulik, U.: Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recogn. 35, 1197\u20131208 (2002)","journal-title":"Pattern Recogn."},{"key":"3_CR14","doi-asserted-by":"publisher","unstructured":"Tam, H., Ng, S., Lui, A.K., Leung, M.: Improved activation schema on automatic clustering using differential evolution algorithm. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1749\u20131756 (2017). https:\/\/doi.org\/10.1109\/CEC.2017.7969513","DOI":"10.1109\/CEC.2017.7969513"},{"key":"3_CR15","doi-asserted-by":"publisher","unstructured":"Kuo, R., Suryani, E., Yasid, A.: Automatic clustering combining differential evolution algorithm and k-means algorithm. In: Proceedings of the Institute of Industrial Engineers Asian Conference 2013, pp. 1207\u20131215 (2013). https:\/\/doi.org\/10.1007\/978-981-4451-98-7","DOI":"10.1007\/978-981-4451-98-7"},{"key":"3_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2016.05.003","volume":"32","author":"AP Piotrowski","year":"2017","unstructured":"Piotrowski, A.P.: Review of differential evolution population size. Swarm Evol. Comput. 32, 1\u201324 (2017). https:\/\/doi.org\/10.1016\/j.swevo.2016.05.003","journal-title":"Swarm Evol. Comput."},{"key":"3_CR17","doi-asserted-by":"publisher","first-page":"1552","DOI":"10.1016\/j.ins.2008.09.024","volume":"179","author":"I Kaya","year":"2009","unstructured":"Kaya, I.: A genetic algorithm approach to determine the sample size for attribute control charts. Inf. Sci. (NY) 179, 1552\u20131566 (2009). https:\/\/doi.org\/10.1016\/j.ins.2008.09.024","journal-title":"Inf. Sci. (NY)"},{"key":"3_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2014.02.001","volume":"17","author":"G Dobbie","year":"2014","unstructured":"Dobbie, G., Sing, Y., Riddle, P., Ur, S.: Research on particle swarm optimization based clustering: a systematic review of literature and techniques. Swarm Evol. Comput. 17, 1\u201313 (2014). https:\/\/doi.org\/10.1016\/j.swevo.2014.02.001","journal-title":"Swarm Evol. Comput."},{"key":"3_CR19","unstructured":"Departamento Administrativo Nacional de Estad\u00edstica: P\u00e1gina principal. Recuperado de: DANE (2018). http:\/\/www.dane.gov.co\/"},{"key":"3_CR20","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1007\/978-3-319-93803-5_18","volume-title":"DMBD 2018","author":"M Torres-Samuel","year":"2018","unstructured":"Torres-Samuel, M., V\u00e1squez, C.L., Viloria, A., Varela, N., Hern\u00e1ndez-Fernandez, L., Portillo-Medina, R.: Analysis of patterns in the university world rankings webometrics, Shanghai, QS and SIR-SCimago: case Latin America. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 188\u2013199. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93803-5_18"},{"issue":"11","key":"3_CR21","first-page":"2963","volume":"12","author":"C V\u00e1squez","year":"2017","unstructured":"V\u00e1squez, C., Torres, M., Viloria, A.: Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J. Eng. Appl. Sci. 12(11), 2963\u20132965 (2017)","journal-title":"J. Eng. Appl. Sci."},{"key":"3_CR22","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/978-3-319-93803-5_22","volume-title":"DMBD 2018","author":"M Torres-Samuel","year":"2018","unstructured":"Torres-Samuel, M., et al.: Efficiency analysis of the visibility of Latin American universities and their impact on the ranking web. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 235\u2013243. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93803-5_22"}],"container-title":["Lecture Notes in Computer Science","Green, Pervasive, and Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-19223-5_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:10:54Z","timestamp":1694776254000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-19223-5_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030192228","9783030192235"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-19223-5_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"27 April 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"GPC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Green, Pervasive, and Cloud Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Uberl\u00e2ndia","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 May 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"gpc2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.gpc2019.facom.ufu.br\/index.html","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"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"38","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"17","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"45% - 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"}},{"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"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}