{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:06:32Z","timestamp":1777485992053,"version":"3.51.4"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T00:00:00Z","timestamp":1732924800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T00:00:00Z","timestamp":1732924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Evolving Systems"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s12530-024-09646-w","type":"journal-article","created":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T06:59:12Z","timestamp":1732949952000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An evolving approach to the similarity-based modeling for online clustering in non-stationary environments"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2176-9110","authenticated-orcid":false,"given":"Nayron Morais","family":"Almeida","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Murilo Osorio","family":"Camargos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Denis G. B.","family":"Mariano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlos H. M.","family":"Bomfim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reinaldo M.","family":"Palhares","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Walmir M.","family":"Caminhas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,30]]},"reference":[{"key":"9646_CR1","doi-asserted-by":"publisher","unstructured":"Aggarwal CC, Yu PS, Han J, Wang J (2003) A framework for clustering evolving data streams. In: Proceedings 2003 VLDB conference: 29th international conference on very large databases (VLDB), pp 81\u201392 https:\/\/doi.org\/10.1016\/B978-012722442-8\/50016-1","DOI":"10.1016\/B978-012722442-8\/50016-1"},{"key":"9646_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/J.ESWA.2019.112947","volume":"141","author":"R Ahmed","year":"2020","unstructured":"Ahmed R, Dalk\u0131l\u0131\u00e7 G, Erten Y (2020) Dgstream: high quality and efficiency stream clustering algorithm. Expert Syst Appl 141:112947. https:\/\/doi.org\/10.1016\/J.ESWA.2019.112947","journal-title":"Expert Syst Appl"},{"issue":"12","key":"9646_CR3","doi-asserted-by":"publisher","first-page":"5320","DOI":"10.3390\/APP11125320","volume":"11","author":"R Al-Amri","year":"2021","unstructured":"Al-Amri R, Murugesan RK, Man M, Abdulateef AF, Al-Sharafi MA, Alkahtani AA (2021) A review of machine learning and deep learning techniques for anomaly detection in IoT data. Appl Sci 11(12):5320. https:\/\/doi.org\/10.3390\/APP11125320","journal-title":"Appl Sci"},{"key":"9646_CR4","doi-asserted-by":"publisher","unstructured":"Angelov P (2014) Anomaly detection based on eccentricity analysis. In: 2014 IEEE symposium on evolving and autonomous learning systems (EALS), pp 1\u20138. https:\/\/doi.org\/10.1109\/EALS.2014.7009497","DOI":"10.1109\/EALS.2014.7009497"},{"key":"9646_CR5","doi-asserted-by":"publisher","DOI":"10.1002\/9780470569962","volume-title":"Evolving intelligent systems: methodology and applications","author":"P Angelov","year":"2010","unstructured":"Angelov P, Filev DP, Kasabov N (2010) Evolving intelligent systems: methodology and applications. Wiley, Hoboken, New Jersey"},{"key":"9646_CR6","doi-asserted-by":"publisher","unstructured":"Batool K, Abbas G (2021) A comprehensive review on evolving data stream clustering. 3rd International Conference on Communication Technologies (ComTech 2021), 138\u2013143 https:\/\/doi.org\/10.1109\/COMTECH52583.2021.9616754","DOI":"10.1109\/COMTECH52583.2021.9616754"},{"issue":"2\u20133","key":"9646_CR7","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","volume":"10","author":"JC Bezdek","year":"1984","unstructured":"Bezdek JC, Ehrlich R, Full W (1984) Fcm: The fuzzy c-means clustering algorithm. Comput Geosci 10(2\u20133):191\u2013203. https:\/\/doi.org\/10.1016\/0098-3004(84)90020-7","journal-title":"Comput Geosci"},{"key":"9646_CR8","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.ins.2019.12.022","volume":"518","author":"CG Bezerra","year":"2020","unstructured":"Bezerra CG, Costa BSJ, Guedes LA, Angelov PP (2020) An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Inf Sci 518:13\u201328. https:\/\/doi.org\/10.1016\/j.ins.2019.12.022","journal-title":"Inf Sci"},{"key":"9646_CR9","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM, Nasrabadi NM (2006) Pattern recognition and machine learning, vol 4. Springer, New York, NY"},{"key":"9646_CR10","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1137\/1.9781611972764.29","volume":"2006","author":"F Cao","year":"2006","unstructured":"Cao F, Ester M, Qian W, Zhou A (2006) Density-based clustering over an evolving data stream with noise. Proc Sixth SIAM Int Confer Data Mining 2006:328\u2013339. https:\/\/doi.org\/10.1137\/1.9781611972764.29","journal-title":"Proc Sixth SIAM Int Confer Data Mining"},{"issue":"3","key":"9646_CR11","doi-asserted-by":"publisher","first-page":"267","DOI":"10.3233\/IFS-1994-2306","volume":"2","author":"SL Chiu","year":"1994","unstructured":"Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2(3):267\u2013278. https:\/\/doi.org\/10.3233\/IFS-1994-2306","journal-title":"J Intell Fuzzy Syst"},{"key":"9646_CR12","doi-asserted-by":"publisher","first-page":"7025","DOI":"10.1007\/S00521-024-09443-1","volume":"36","author":"B Erdin\u00e7","year":"2024","unstructured":"Erdin\u00e7 B, Kaya M, \u015eenol A (2024) Mcmststream: applying minimum spanning tree to kd-tree-based micro-clusters to define arbitrary-shaped clusters in streaming data. Neural Comput Appl 36:7025\u20137042. https:\/\/doi.org\/10.1007\/S00521-024-09443-1","journal-title":"Neural Comput Appl"},{"key":"9646_CR13","unstructured":"Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. KDD\u201996, pp. 226\u2013231. AAAI Press, Portland, Oregon"},{"key":"9646_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104743","volume":"110","author":"AE Ezugwu","year":"2022","unstructured":"Ezugwu AE, Ikotun AM, Oyelade OO, Abualigah L, Agushaka JO, Eke CI, Akinyelu AA (2022) A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Eng Appl Artif Intell 110:104743. https:\/\/doi.org\/10.1016\/j.engappai.2022.104743","journal-title":"Eng Appl Artif Intell"},{"key":"9646_CR15","doi-asserted-by":"publisher","first-page":"2215","DOI":"10.1109\/TCYB.2018.2822552","volume":"49","author":"C Fahy","year":"2019","unstructured":"Fahy C, Yang S, Gongora M (2019) Ant colony stream clustering: a fast density clustering algorithm for dynamic data streams. IEEE Trans Cybernet 49:2215\u20132228. https:\/\/doi.org\/10.1109\/TCYB.2018.2822552","journal-title":"IEEE Trans Cybernet"},{"key":"9646_CR16","doi-asserted-by":"publisher","DOI":"10.1145\/2523813","author":"J Gama","year":"2014","unstructured":"Gama J, Zliobaite I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv (CSUR). https:\/\/doi.org\/10.1145\/2523813","journal-title":"ACM Comput Surv (CSUR)"},{"key":"9646_CR17","doi-asserted-by":"publisher","first-page":"4649","DOI":"10.32604\/CMC.2023.035987","volume":"75","author":"NLA Ghani","year":"2023","unstructured":"Ghani NLA, Aziz IA, AbdulKadir SJ (2023) Subspace clustering in high-dimensional data streams: a systematic literature review. Comput Mater Contin 75:4649\u20134668. https:\/\/doi.org\/10.32604\/CMC.2023.035987","journal-title":"Comput Mater Contin"},{"key":"9646_CR18","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1109\/TKDE.2016.2522412","volume":"28","author":"M Hahsler","year":"2016","unstructured":"Hahsler M, Bolaos M (2016) Clustering data streams based on shared density between micro-clusters. IEEE Trans Knowl Data Eng 28:1449\u20131461. https:\/\/doi.org\/10.1109\/TKDE.2016.2522412","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9646_CR19","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.ins.2016.12.004","volume":"382\u2013383","author":"R Hyde","year":"2016","unstructured":"Hyde R, Angelov P, Mackenzie AR (2016) Fully online clustering of evolving data streams into arbitrarily shaped clusters. Inf Sci 382\u2013383:96\u2013114. https:\/\/doi.org\/10.1016\/j.ins.2016.12.004","journal-title":"Inf Sci"},{"key":"9646_CR20","doi-asserted-by":"publisher","unstructured":"Isaksson C, Dunham MH, Hahsler M (2012) Sostream: Self organizing density-based clustering over data stream. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7376 LNAI, pp 264\u2013278 https:\/\/doi.org\/10.1007\/978-3-642-31537-4_21","DOI":"10.1007\/978-3-642-31537-4_21"},{"key":"9646_CR21","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/BF00337288\/METRICS","volume":"43","author":"T Kohonen","year":"1982","unstructured":"Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43:59\u201369. https:\/\/doi.org\/10.1007\/BF00337288\/METRICS","journal-title":"Biol Cybern"},{"key":"9646_CR22","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/J.KNOSYS.2016.02.004","volume":"99","author":"S Laohakiat","year":"2016","unstructured":"Laohakiat S, Phimoltares S, Lursinsap C (2016) Hyper-cylindrical micro-clustering for streaming data with unscheduled data removals. Knowl-Based Syst 99:183\u2013200. https:\/\/doi.org\/10.1016\/J.KNOSYS.2016.02.004","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"9646_CR23","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","volume":"28","author":"S Lloyd","year":"1982","unstructured":"Lloyd S (1982) Least squares quantization in pcm. IEEE Trans Inf Theory 28(2):129\u2013137. https:\/\/doi.org\/10.1109\/TIT.1982.1056489","journal-title":"IEEE Trans Inf Theory"},{"key":"9646_CR24","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/J.INS.2015.01.010","volume":"304","author":"E Lughofer","year":"2015","unstructured":"Lughofer E, Sayed-Mouchaweh M (2015) Autonomous data stream clustering implementing split-and-merge concepts - towards a plug-and-play approach. Inf Sci 304:54\u201379. https:\/\/doi.org\/10.1016\/J.INS.2015.01.010","journal-title":"Inf Sci"},{"key":"9646_CR25","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1016\/j.future.2020.01.017","volume":"106","author":"J Maia","year":"2020","unstructured":"Maia J, Severiano Junior CA, Gadelha Guimar\u00e3es F, Leite De Castro C, Lemos P, Camilo J, Galindo F, Cohen W (2020) Evolving clustering algorithm based on mixture of typicalities for stream data mining. Futur Gener Comput Syst 106:672\u2013684. https:\/\/doi.org\/10.1016\/j.future.2020.01.017","journal-title":"Futur Gener Comput Syst"},{"issue":"4","key":"9646_CR26","doi-asserted-by":"publisher","first-page":"1913","DOI":"10.1016\/j.jfranklin.2017.07.038","volume":"355","author":"MA Marins","year":"2018","unstructured":"Marins MA, Ribeiro FM, Netto SL, Silva EA (2018) Improved similarity-based modeling for the classification of rotating-machine failures. J Franklin Inst 355(4):1913\u20131930. https:\/\/doi.org\/10.1016\/j.jfranklin.2017.07.038","journal-title":"J Franklin Inst"},{"key":"9646_CR27","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1108\/02602280510585691","volume":"2","author":"G Monkman","year":"2005","unstructured":"Monkman G, Wegerich S (2005) Similarity-based modeling of vibration features for fault detection and identification. Sens Rev 2:114\u2013122. https:\/\/doi.org\/10.1108\/02602280510585691","journal-title":"Sens Rev"},{"key":"9646_CR28","doi-asserted-by":"publisher","unstructured":"Moshtaghi M, Leckie C, Bezdek JC (2016) Online clustering of multivariate time-series. In: Proceedings of the 2016 SIAM international conference on data mining (SDM), pp 360\u2013368. https:\/\/doi.org\/10.1137\/1.9781611974348.41","DOI":"10.1137\/1.9781611974348.41"},{"key":"9646_CR29","doi-asserted-by":"publisher","unstructured":"Moshtaghi M, Leckie C, Karunasekera S, Bezdek JC, Rajasegarar S, Palaniswami M (2011) Incremental elliptical boundary estimation for anomaly detection in wireless sensor networks. In: Proceedings - IEEE international conference on data mining, ICDM, 467\u2013476 https:\/\/doi.org\/10.1109\/ICDM.2011.80","DOI":"10.1109\/ICDM.2011.80"},{"key":"9646_CR30","doi-asserted-by":"crossref","unstructured":"Perez A, Jaramillo F, Quintero V, Orchard M (2018) Characterizing the degradation process of lithium-ion batteries using a similarity-based-modeling approach. In: PHM society European conference, vol 4","DOI":"10.36001\/phme.2018.v4i1.439"},{"key":"9646_CR31","doi-asserted-by":"publisher","unstructured":"Perez A, Rozas H, Jaramillo F, Quintero V, Orchard M (2019) A simulation engine for the characterization of capacity degradation processes in lithium-ion batteries undergoing heterogeneous operating conditions. In: Proceedings of the annual conference of the PHM society, vol 11. https:\/\/doi.org\/10.36001\/phmconf.2019.v11i1.855","DOI":"10.36001\/phmconf.2019.v11i1.855"},{"key":"9646_CR32","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/J.MEASUREMENT.2018.11.041","volume":"144","author":"KSS Reddy","year":"2019","unstructured":"Reddy KSS, Bindu CS (2019) Streamsw: a density-based approach for clustering data streams over sliding windows. Measurement 144:14\u201319. https:\/\/doi.org\/10.1016\/J.MEASUREMENT.2018.11.041","journal-title":"Measurement"},{"key":"9646_CR33","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/J.PATCOG.2019.05.024","volume":"94","author":"NB Roa","year":"2019","unstructured":"Roa NB, Trav\u00e8-Massuy\u00e8s L, Grisales-Palacio VH (2019) Dyclee: dynamic clustering for tracking evolving environments. Pattern Recogn 94:162\u2013186. https:\/\/doi.org\/10.1016\/J.PATCOG.2019.05.024","journal-title":"Pattern Recogn"},{"key":"9646_CR34","doi-asserted-by":"publisher","unstructured":"Rolewicz S (1987) Functional analysis and control theory: linear systems. Mathematics and its applications, East European Series, vol 29. Springer, Dordrecht. https:\/\/doi.org\/10.1007\/978-94-015-7758-8 . SpringerLink (Online service)","DOI":"10.1007\/978-94-015-7758-8"},{"key":"9646_CR35","unstructured":"Singer RM, Gross KC, Herzog JP, King RW, Wegerich S (1997) Model-based nuclear power plant monitoring and fault detection: Theoretical foundations. In: Proceedings of the international conference on intelligent systems applications to power systems, Seoul, Korea"},{"key":"9646_CR36","volume-title":"Linear Algebra and Its Applications","author":"G Strang","year":"2006","unstructured":"Strang G (2006) Linear Algebra and Its Applications. Thomson, Brooks\/Cole, Belmont, CA"},{"key":"9646_CR37","doi-asserted-by":"publisher","unstructured":"Tobar FA, Yacher L, Paredes R, Orchard ME (2011) Anomaly detection in power generation plants using similarity-based modeling and multivariate analysis. In: Proceedings of the 2011 American control conference, pp 1940\u20131945. https:\/\/doi.org\/10.1109\/ACC.2011.5991323","DOI":"10.1109\/ACC.2011.5991323"},{"key":"9646_CR38","volume-title":"Sequential Analysis","author":"A Wald","year":"2004","unstructured":"Wald A (2004) Sequential Analysis. Courier Corporation, Mineola, New York"},{"key":"9646_CR39","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1007\/S00357-017-9222-1\/METRICS","volume":"34","author":"N Wattanakitrungroj","year":"2017","unstructured":"Wattanakitrungroj N, Maneeroj S, Lursinsap C (2017) Versatile hyper-elliptic clustering approach for streaming data based on one-pass-thrown-away learning. J Classif 34:108\u2013147. https:\/\/doi.org\/10.1007\/S00357-017-9222-1\/METRICS","journal-title":"J Classif"},{"key":"9646_CR40","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/J.DATAK.2018.07.002","volume":"117","author":"N Wattanakitrungroj","year":"2018","unstructured":"Wattanakitrungroj N, Maneeroj S, Lursinsap C (2018) Bestream: batch capturing with elliptic function for one-pass data stream clustering. Data Knowl Eng 117:53\u201370. https:\/\/doi.org\/10.1016\/J.DATAK.2018.07.002","journal-title":"Data Knowl Eng"},{"key":"9646_CR41","doi-asserted-by":"publisher","unstructured":"Wegerich SW (2004) Similarity based modeling of time synchronous averaged vibration signals for machinery health monitoring. In: 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No. 04TH8720), vol. 6, pp. 3654\u20133662. https:\/\/doi.org\/10.1109\/AERO.2004.1368182","DOI":"10.1109\/AERO.2004.1368182"},{"key":"9646_CR42","doi-asserted-by":"publisher","unstructured":"Wegerich SW, Wilks AD, Pipke RM (2003) Nonparametric modeling of vibration signal features for equipment health monitoring. In: 2003 IEEE aerospace conference proceedings (Cat. No.03TH8652), vol 7, pp 3113\u20133121. https:\/\/doi.org\/10.1109\/AERO.2003.1234154","DOI":"10.1109\/AERO.2003.1234154"},{"key":"9646_CR43","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","volume":"8","author":"LA Zadeh","year":"1965","unstructured":"Zadeh LA (1965) Fuzzy sets. Inf Control 8:338\u2013353. https:\/\/doi.org\/10.1016\/S0019-9958(65)90241-X","journal-title":"Inf Control"},{"key":"9646_CR44","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1007\/s10462-020-09874-x","volume":"54","author":"A Zubaro\u011flu","year":"2020","unstructured":"Zubaro\u011flu A, Atalay V (2020) Data stream clustering: a review. Artif Intell Rev 54:1201\u20131236. https:\/\/doi.org\/10.1007\/s10462-020-09874-x","journal-title":"Artif Intell Rev"}],"container-title":["Evolving Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-024-09646-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12530-024-09646-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-024-09646-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T07:08:07Z","timestamp":1732950487000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12530-024-09646-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,30]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["9646"],"URL":"https:\/\/doi.org\/10.1007\/s12530-024-09646-w","relation":{},"ISSN":["1868-6478","1868-6486"],"issn-type":[{"value":"1868-6478","type":"print"},{"value":"1868-6486","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,30]]},"assertion":[{"value":"26 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors affirm that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"16"}}