{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T05:06:46Z","timestamp":1768799206280,"version":"3.49.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071379, 62106196 and 62071378"],"award-info":[{"award-number":["62071379, 62106196 and 62071378"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2022JM- 333"],"award-info":[{"award-number":["2022JM- 333"]}]},{"name":"The Youth Innovation Team of Shaanxi Universities"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s10489-024-05813-3","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T11:01:32Z","timestamp":1727780492000},"page":"12791-12818","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Dynamic noise self-recovery ECM clustering algorithm with adaptive spatial constraints for image segmentation"],"prefix":"10.1007","volume":"54","author":[{"given":"Rong","family":"Lan","sequence":"first","affiliation":[]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaoying","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Haowen","family":"Mi","sequence":"additional","affiliation":[]},{"given":"Haiyan","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"key":"5813_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijar.2022.07.001","volume":"149","author":"B Ali","year":"2022","unstructured":"Ali B, Azam N, Yao JT (2022) A three-way clustering approach using image enhancement operations. International Journal of Approximate Reasoning 149:1\u201338. https:\/\/doi.org\/10.1016\/j.ijar.2022.07.001","journal-title":"International Journal of Approximate Reasoning"},{"issue":"13","key":"5813_CR2","doi-asserted-by":"publisher","first-page":"16487","DOI":"10.1007\/s10489-022-04315-4","volume":"53","author":"I Khatri","year":"2023","unstructured":"Khatri I, Kumar D, Gupta A (2023) A noise robust kernel fuzzy clustering based on picture fuzzy sets and kl divergence measure for mri image segmentation. Appl Intell 53(13):16487\u201316518. https:\/\/doi.org\/10.1007\/s10489-022-04315-4","journal-title":"Appl Intell"},{"issue":"1","key":"5813_CR3","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s10489-022-03255-3","volume":"53","author":"L Lei","year":"2023","unstructured":"Lei L, Wu C, Tian X (2023) Robust deep kernel-based fuzzy clustering with spatial information for image segmentation. Appl Intell 53(1):23\u201348. https:\/\/doi.org\/10.1007\/s10489-022-03255-3","journal-title":"Appl Intell"},{"issue":"6","key":"5813_CR4","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.1109\/TVCG.2017.2702738","volume":"24","author":"Q Guo","year":"2017","unstructured":"Guo Q, Gao S, Zhang X, Yin Y, Zhang C (2017) Patch-based image inpainting via two-stage low rank approximation. IEEE Trans Visual Comput Graphics 24(6):2023\u20132036. https:\/\/doi.org\/10.1109\/TVCG.2017.2702738","journal-title":"IEEE Trans Visual Comput Graphics"},{"key":"5813_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2022.11.008","author":"L Huang","year":"2022","unstructured":"Huang L, Ruan S (2022) Application of belief functions to medical image segmentation: A review. Information fusion. https:\/\/doi.org\/10.1016\/j.inffus.2022.11.008","journal-title":"Information fusion"},{"key":"5813_CR6","doi-asserted-by":"publisher","unstructured":"Ammar\u00a0K Al-Musawi, Fatih Anayi, and Michael Packianather. Three-phase induction motor fault detection based on thermal image segmentation. Infrared Physics & Technology 104:103140, 2020. https:\/\/doi.org\/10.1016\/j.infrared.2019.103140","DOI":"10.1016\/j.infrared.2019.103140"},{"issue":"10","key":"5813_CR7","doi-asserted-by":"publisher","first-page":"11654","DOI":"10.1007\/s10489-022-04064-4","volume":"53","author":"L Abualigah","year":"2023","unstructured":"Abualigah L, Almotairi KH, Elaziz MA (2023) Multilevel thresholding image segmentation using meta-heuristic optimization algorithms: Comparative analysis, open challenges and new trends. Appl Intell 53(10):11654\u201311704. https:\/\/doi.org\/10.1007\/s10489-022-04064-4","journal-title":"Appl Intell"},{"key":"5813_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2022.169039","volume":"260","author":"B Ji","year":"2022","unstructured":"Ji B, Hu X, Ding F, Ji Y, Gao H (2022) An effective color image segmentation approach using superpixel-neutrosophic c-means clustering and gradient-structural similarity. Optik 260:169039. https:\/\/doi.org\/10.1016\/j.ijleo.2022.169039","journal-title":"Optik"},{"issue":"8","key":"5813_CR9","doi-asserted-by":"publisher","first-page":"5280","DOI":"10.1007\/s10489-020-01977-w","volume":"51","author":"H Liu","year":"2021","unstructured":"Liu H, Zhao F (2021) Multiobjective fuzzy clustering with multiple spatial information for noisy color image segmentation. Appl Intell 51(8):5280\u20135298. https:\/\/doi.org\/10.1007\/s10489-020-01977-w","journal-title":"Appl Intell"},{"issue":"6","key":"5813_CR10","doi-asserted-by":"publisher","first-page":"4490","DOI":"10.1007\/s10489-024-05372-7","volume":"54","author":"X Zhang","year":"2024","unstructured":"Zhang X, Pu L, Wan L, Wang X, Zhou Y (2024) Ds-msff-net: Dual-path self-attention multi-scale feature fusion network for ct image segmentation. Appl Intell 54(6):4490\u20134506. https:\/\/doi.org\/10.1007\/s10489-024-05372-7","journal-title":"Appl Intell"},{"key":"5813_CR11","doi-asserted-by":"publisher","unstructured":"H Mittal, AC Pandey, M Saraswat, S Kumar, R Pal, G Modwel. A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets. Multimedia Tools and Applications pages 1\u201326, 2021. https:\/\/doi.org\/10.1007\/s11042-021-10594-9","DOI":"10.1007\/s11042-021-10594-9"},{"issue":"4","key":"5813_CR12","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.1016\/j.patcog.2007.08.014","volume":"41","author":"MH Masson","year":"2008","unstructured":"Masson MH, Denoeux T (2008) Ecm: An evidential version of the fuzzy c-means algorithm. Pattern Recogn 41(4):1384\u20131397. https:\/\/doi.org\/10.1016\/j.patcog.2007.08.014","journal-title":"Pattern Recogn"},{"issue":"2","key":"5813_CR13","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.patrec.2003.09.008","volume":"25","author":"MH Masson","year":"2004","unstructured":"Masson MH, Denoeux T (2004) Clustering interval-valued proximity data using belief functions. Pattern Recogn Lett 25(2):163\u2013171. https:\/\/doi.org\/10.1016\/j.patrec.2003.09.008","journal-title":"Pattern Recogn Lett"},{"key":"5813_CR14","doi-asserted-by":"publisher","unstructured":"Liu Z, Letchmunan S (2024) Representing uncertainty and imprecision in machine learning: A survey on belief functions. J King Saud Univ Comput Inf Sci, page 101904. https:\/\/doi.org\/10.1016\/j.jksuci.2023.101904","DOI":"10.1016\/j.jksuci.2023.101904"},{"issue":"3","key":"5813_CR15","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.patrec.2011.10.011","volume":"33","author":"ZG Liu","year":"2012","unstructured":"Liu ZG, Dezert J, Mercier G, Pan Q (2012) Belief c-means: An extension of fuzzy c-means algorithm in belief functions framework. Pattern Recogn Lett 33(3):291\u2013300. https:\/\/doi.org\/10.1016\/j.patrec.2011.10.011","journal-title":"Pattern Recogn Lett"},{"key":"5813_CR16","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.knosys.2014.11.013","volume":"74","author":"Z Liu","year":"2015","unstructured":"Liu Z, Pan Q, Dezert J, Mercier G (2015) Credal c-means clustering method based on belief functions. Knowl-Based Syst 74:119\u2013132. https:\/\/doi.org\/10.1016\/j.knosys.2014.11.013","journal-title":"Knowl-Based Syst"},{"key":"5813_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106643","volume":"213","author":"Z Zhang","year":"2021","unstructured":"Zhang Z, Liu Z, Martin A, Liu Z, Zhou K (2021) Dynamic evidential clustering algorithm. Knowl-Based Syst 213:106643. https:\/\/doi.org\/10.1016\/j.knosys.2020.106643","journal-title":"Knowl-Based Syst"},{"key":"5813_CR18","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ins.2020.04.014","volume":"528","author":"T Denoeux","year":"2020","unstructured":"Denoeux T (2020) Calibrated model-based evidential clustering using bootstrapping. Inf Sci 528:17\u201345. https:\/\/doi.org\/10.1016\/j.ins.2020.04.014","journal-title":"Inf Sci"},{"key":"5813_CR19","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.ins.2021.05.011","volume":"572","author":"T Denoeux","year":"2021","unstructured":"Denoeux T (2021) Nn-evclus: Neural network-based evidential clustering. Inf Sci 572:297\u2013330. https:\/\/doi.org\/10.1016\/j.ins.2021.05.011","journal-title":"Inf Sci"},{"key":"5813_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107751","volume":"113","author":"C Gong","year":"2021","unstructured":"Gong C, Su Z, Wang P, Wang Q (2021) An evidential clustering algorithm by finding belief-peaks and disjoint neighborhoods. Pattern Recogn 113:107751. https:\/\/doi.org\/10.1016\/j.patcog.2020.107751","journal-title":"Pattern Recogn"},{"issue":"6","key":"5813_CR21","doi-asserted-by":"publisher","first-page":"1907","DOI":"10.1007\/s41066-023-00410-0","volume":"8","author":"Z Liu","year":"2023","unstructured":"Liu Z (2023) Credal-based fuzzy number data clustering. Granular Computing 8(6):1907\u20131924. https:\/\/doi.org\/10.1007\/s41066-023-00410-0","journal-title":"Granular Computing"},{"key":"5813_CR22","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.ijar.2021.03.008","volume":"133","author":"V Antoine","year":"2021","unstructured":"Antoine V, Guerrero JA, Xie J (2021) Fast semi-supervised evidential clustering. Int J Approximate Reasoning 133:116\u2013132. https:\/\/doi.org\/10.1016\/j.ijar.2021.03.008","journal-title":"Int J Approximate Reasoning"},{"issue":"3","key":"5813_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3638061","volume":"18","author":"Z Liu","year":"2024","unstructured":"Liu Z, Letchmunan S (2024) Enhanced fuzzy clustering for incomplete instance with evidence combination. ACM Trans Knowl Discov Data 18(3):1\u201320. https:\/\/doi.org\/10.1145\/3638061","journal-title":"ACM Trans Knowl Discov Data"},{"key":"5813_CR24","doi-asserted-by":"publisher","unstructured":"Z Liu, H Huang, S Letchmunan (2023) Adaptive weighted multi-view evidential clustering. In: International Conference on Artificial Neural Networks, pp. 265\u2013277, https:\/\/doi.org\/10.1007\/978-3-031-44216-2_22","DOI":"10.1007\/978-3-031-44216-2_22"},{"key":"5813_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111770","volume":"294","author":"Z Liu","year":"2024","unstructured":"Liu Z, Huang H, Letchmunan S, Deveci M (2024) Adaptive weighted multi-view evidential clustering with feature preference. Knowl-Based Syst 294:111770. https:\/\/doi.org\/10.1016\/j.knosys.2024.111770","journal-title":"Knowl-Based Syst"},{"key":"5813_CR26","doi-asserted-by":"publisher","unstructured":"L Rong, M Haowen, Q Na, Z Feng, Y Haiyan, Z Lu (2023) Adaptive kernelized evidence c-means clustering combining spatial information for noisy image segmentation. In: 2023 5th International Conference on Natural Language Processing (ICNLP), pp. 42\u201351 https:\/\/doi.org\/10.1109\/ICNLP58431.2023.00016","DOI":"10.1109\/ICNLP58431.2023.00016"},{"key":"5813_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109619","volume":"129","author":"L Jiao","year":"2022","unstructured":"Jiao L, Den\u0153ux T, Liu ZG, Pan Q (2022) Egmm: An evidential version of the gaussian mixture model for clustering. Appl Soft Comput 129:109619. https:\/\/doi.org\/10.1016\/j.asoc.2022.109619","journal-title":"Appl Soft Comput"},{"key":"5813_CR28","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/s00530-017-0579-0","volume":"25","author":"F Wang","year":"2019","unstructured":"Wang F, Lian C, Vera P, Ruan S (2019) Adaptive kernelized evidential clustering for automatic 3d tumor segmentation in fdg-pet images. Multimedia Syst 25:127\u2013133. https:\/\/doi.org\/10.1007\/s00530-017-0579-0","journal-title":"Multimedia Syst"},{"key":"5813_CR29","doi-asserted-by":"publisher","unstructured":"B Lelandais, S Ruan, T Den\u0153ux, P Vera, I Gardin. Fusion of multi-tracer pet images for dose painting.Medical image analysis 18(7):1247\u20131259, 2014. https:\/\/doi.org\/10.1016\/j.media.2014.06.014","DOI":"10.1016\/j.media.2014.06.014"},{"issue":"2","key":"5813_CR30","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1109\/TIP.2018.2872908","volume":"28","author":"C Lian","year":"2018","unstructured":"Lian C, Ruan S, Den\u0153ux T, Li H, Vera P (2018) Joint tumor segmentation in pet-ct images using co-clustering and fusion based on belief functions. IEEE Trans Image Process 28(2):755\u2013766. https:\/\/doi.org\/10.1109\/TIP.2018.2872908","journal-title":"IEEE Trans Image Process"},{"issue":"2\u20133","key":"5813_CR31","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. Computers & geosciences 10(2\u20133):191\u2013203. https:\/\/doi.org\/10.1016\/0098-3004(84)90020-7","journal-title":"Computers & geosciences"},{"issue":"3","key":"5813_CR32","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1109\/42.996338","volume":"21","author":"MN Ahmed","year":"2002","unstructured":"Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T (2002) A modified fuzzy c-means algorithm for bias field estimation and segmentation of mri data. IEEE Trans Med Imaging 21(3):193\u2013199. https:\/\/doi.org\/10.1109\/42.996338","journal-title":"IEEE Trans Med Imaging"},{"key":"5813_CR33","doi-asserted-by":"publisher","unstructured":"S Chen, D Zhang. Robust image segmentation using fcm with spatial constraints based on new kernel-induced distance measure. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(4):1907\u20131916, 2004. https:\/\/doi.org\/10.1109\/TSMCB.2004.831165","DOI":"10.1109\/TSMCB.2004.831165"},{"issue":"5","key":"5813_CR34","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.1109\/TIP.2010.2040763","volume":"19","author":"S Krinidis","year":"2010","unstructured":"Krinidis S, Chatzis V (2010) A robust fuzzy local information c-means clustering algorithm. IEEE Trans Image Process 19(5):1328\u20131337. https:\/\/doi.org\/10.1109\/TIP.2010.2040763","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"5813_CR35","doi-asserted-by":"publisher","first-page":"3027","DOI":"10.1109\/TFUZZ.2018.2796074","volume":"26","author":"T Lei","year":"2018","unstructured":"Lei T, Jia X, Zhang Y, He L, Meng H, Nandi AK (2018) Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering. IEEE Trans Fuzzy Syst 26(5):3027\u20133041. https:\/\/doi.org\/10.1109\/TFUZZ.2018.2796074","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"5813_CR36","doi-asserted-by":"publisher","unstructured":"K Miao, J Wang, Y Gao, C Cao, Y Xie, P Gao (2021) Robust fuzzy clustering algorithm based on adaptive neighbors. In: Journal of Physics: Conference Series, vol. 2025, page 012046, https:\/\/doi.org\/10.1088\/1742-6596\/2025\/1\/012046","DOI":"10.1088\/1742-6596\/2025\/1\/012046"},{"issue":"2","key":"5813_CR37","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1109\/TIP.2012.2219547","volume":"22","author":"M Gong","year":"2012","unstructured":"Gong M, Liang Y, Shi J, Ma W, Ma J (2012) Fuzzy c-means clustering with local information and kernel metric for image segmentation. IEEE Trans Image Process 22(2):573\u2013584. https:\/\/doi.org\/10.1109\/TIP.2012.2219547","journal-title":"IEEE Trans Image Process"},{"key":"5813_CR38","doi-asserted-by":"publisher","unstructured":"Yu H, Xie S, Fan J, Lan R, Lei B (2024) Mahalanobis-kernel distance-based suppressed possibilistic c-means clustering algorithm for imbalanced image segmentation. IEEE Trans Fuzzy Syst 5. https:\/\/doi.org\/10.1109\/TFUZZ.2024.3405497","DOI":"10.1109\/TFUZZ.2024.3405497"},{"key":"5813_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110736","volume":"276","author":"H Yu","year":"2023","unstructured":"Yu H, Jiang L, Fan J, Lan R (2023) Double-suppressed possibilistic fuzzy gustafson-kessel clustering algorithm. Knowl-Based Syst 276:110736. https:\/\/doi.org\/10.1016\/j.knosys.2023.110736","journal-title":"Knowl-Based Syst"},{"key":"5813_CR40","doi-asserted-by":"publisher","unstructured":"Yu H, Jiang L, Fan J, Xie S, Lan R (2024) A feature-weighted suppressed possibilistic fuzzy c-means clustering algorithm and its application on color image segmentation. Expert Syst Appl 241:122270. https:\/\/doi.org\/10.1016\/j.eswa.2023.122270","DOI":"10.1016\/j.eswa.2023.122270"},{"key":"5813_CR41","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s11704-010-0393-8","volume":"5","author":"F Zhao","year":"2011","unstructured":"Zhao F, Jiao L, Liu H (2011) Fuzzy c-means clustering with non local spatial information for noisy image segmentation. Frontiers of Computer Science in China 5:45\u201356. https:\/\/doi.org\/10.1007\/s11704-010-0393-8","journal-title":"Frontiers of Computer Science in China"},{"key":"5813_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106318","volume":"92","author":"Q Wang","year":"2020","unstructured":"Wang Q, Wang X, Fang C, Yang W (2020) Robust fuzzy c-means clustering algorithm with adaptive spatial & intensity constraint and membership linking for noise image segmentation. Appl Soft Comput 92:106318. https:\/\/doi.org\/10.1016\/j.asoc.2020.106318","journal-title":"Appl Soft Comput"},{"key":"5813_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104672","volume":"110","author":"T Wei","year":"2022","unstructured":"Wei T, Wang X, Li X, Zhu S (2022) Fuzzy subspace clustering noisy image segmentation algorithm with adaptive local variance & non-local information and mean membership linking. Eng Appl Artif Intell 110:104672. https:\/\/doi.org\/10.1016\/j.engappai.2022.104672","journal-title":"Eng Appl Artif Intell"},{"key":"5813_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijar.2023.02.013","volume":"157","author":"T Wei","year":"2023","unstructured":"Wei T, Wang X, Wu J, Zhu S (2023) Interval type-2 possibilistic fuzzy clustering noisy image segmentation algorithm with adaptive spatial constraints and local feature weighting & clustering weighting. Int J Approximate Reasoning 157:1\u201332. https:\/\/doi.org\/10.1016\/j.ijar.2023.02.013","journal-title":"Int J Approximate Reasoning"},{"issue":"2","key":"5813_CR45","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1109\/TPAMI.2011.130","volume":"34","author":"S Alpert","year":"2011","unstructured":"Alpert S, Galun M, Brandt A, Basri R (2011) Image segmentation by probabilistic bottom-up aggregation and cue integration. IEEE Trans Pattern Anal Mach Intell 34(2):315\u2013327. https:\/\/doi.org\/10.1109\/TPAMI.2011.130","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5813_CR46","doi-asserted-by":"publisher","unstructured":"Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, and Dacheng Tao. Contrastive boundary learning for point cloud segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 8489\u20138499, 2022. https:\/\/doi.org\/10.1109\/CVPR52688.2022.00830","DOI":"10.1109\/CVPR52688.2022.00830"},{"issue":"4","key":"5813_CR47","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612. https:\/\/doi.org\/10.1109\/TIP.2003.819861","journal-title":"IEEE Trans Image Process"},{"key":"5813_CR48","doi-asserted-by":"publisher","unstructured":"NX Vinh, J Epps, J Bailey. Information theoretic measures for clusterings comparison: is a correction for chance necessary? In: Proceedings of the 26th annual international conference on machine learning, pages 1073\u20131080, 2009. https:\/\/doi.org\/10.1007\/s10846-010-9415-x","DOI":"10.1007\/s10846-010-9415-x"},{"issue":"3","key":"5813_CR49","doi-asserted-by":"publisher","first-page":"485","DOI":"10.3233\/IDA-130590","volume":"17","author":"TF Covoes","year":"2013","unstructured":"Covoes TF, Hruschka ER, Ghosh J (2013) A study of k-means-based algorithms for constrained clustering. Intelligent Data Analysis 17(3):485\u2013505. https:\/\/doi.org\/10.3233\/IDA-130590","journal-title":"Intelligent Data Analysis"},{"issue":"1","key":"5813_CR50","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.eij.2019.10.005","volume":"21","author":"M Alruwaili","year":"2020","unstructured":"Alruwaili M, Siddiqi MH, Javed MA (2020) A robust clustering algorithm using spatial fuzzy c-means for brain mr images. Egyptian Informatics Journal 21(1):51\u201366. https:\/\/doi.org\/10.1016\/j.eij.2019.10.005","journal-title":"Egyptian Informatics Journal"},{"key":"5813_CR51","doi-asserted-by":"publisher","unstructured":"JC Bezdek. Mathematical models for systematics and taxonomy. In: Proceedings of the 8th International Conference on Numerical Taxonomy, 1975, 1975. https:\/\/doi.org\/10.1002\/9780470696590.ch6","DOI":"10.1002\/9780470696590.ch6"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05813-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05813-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05813-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T02:05:43Z","timestamp":1731377143000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05813-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,1]]},"references-count":51,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["5813"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05813-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,1]]},"assertion":[{"value":"22 August 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. And this article does not contain any studies with animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}