{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T16:18:56Z","timestamp":1784391536131,"version":"3.55.0"},"reference-count":78,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Computing"],"DOI":"10.1007\/s10791-025-09639-6","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T09:52:45Z","timestamp":1751363565000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An effective imputation approach for handling missing data using intuitionistic fuzzy clustering algorithms"],"prefix":"10.1007","volume":"28","author":[{"given":"Kavita","family":"Sethia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaspreeti","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anjana","family":"Gosain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"key":"9639_CR1","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.jksuci.2018.01.006","volume":"31","author":"A Nekouie","year":"2019","unstructured":"Nekouie A, Moattar MH. Missing value imputation for breast cancer diagnosis data using tensor factorization improved by enhanced reduced adaptive particle swarm optimization. J King Saud Univ Inf Sci. 2019;31:287\u201394.","journal-title":"J King Saud Univ Inf Sci"},{"key":"9639_CR2","doi-asserted-by":"publisher","first-page":"18325","DOI":"10.1007\/s00521-022-07702-7","volume":"34","author":"FA Adnan","year":"2022","unstructured":"Adnan FA, Jamaludin KR, Wan Muhamad WZA, Miskon S. A review of the current publication trends on missing data imputation over three decades: direction and future research. Neural Comput Appl. 2022;34:18325\u201340.","journal-title":"Neural Comput Appl"},{"key":"9639_CR3","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1007\/s10462-019-09709-4","volume":"53","author":"W-C Lin","year":"2020","unstructured":"Lin W-C, Tsai C-F. Missing value imputation: a review and analysis of the literature (2006\u20132017). Artif Intell Rev. 2020;53:1487\u2013509.","journal-title":"Artif Intell Rev"},{"key":"9639_CR4","first-page":"727","volume":"42","author":"S Goel","year":"2022","unstructured":"Goel S, Tushir M. A new semi-supervised clustering for incomplete data. J Intell Fuzzy Syst. 2022;42:727\u201339.","journal-title":"J Intell Fuzzy Syst"},{"key":"9639_CR5","doi-asserted-by":"publisher","first-page":"9701","DOI":"10.1007\/s00521-022-06958-3","volume":"34","author":"SE Awan","year":"2022","unstructured":"Awan SE, Bennamoun M, Sohel F, Sanfilippo F, Dwivedi G. A reinforcement learning-based approach for imputing missing data. Neural Comput Appl. 2022;34:9701\u201316.","journal-title":"Neural Comput Appl"},{"key":"9639_CR6","doi-asserted-by":"publisher","first-page":"100799","DOI":"10.1016\/j.imu.2021.100799","volume":"27","author":"MK Hasan","year":"2021","unstructured":"Hasan MK, Alam MA, Roy S, Dutta A, Jawad MT, Das S. Missing value imputation affects the performance of machine learning: a review and analysis of the literature (2010\u20132021). Informatics Med Unlocked. 2021;27:100799.","journal-title":"Informatics Med. Unlocked"},{"key":"9639_CR7","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.patcog.2017.04.005","volume":"69","author":"J Xia","year":"2017","unstructured":"Xia J, et al. Adjusted weight voting algorithm for random forests in handling missing values. Pattern Recognit. 2017;69:52\u201360.","journal-title":"Pattern Recognit"},{"key":"9639_CR8","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1109\/TSMCA.2007.902631","volume":"37","author":"A Farhangfar","year":"2007","unstructured":"Farhangfar A, Kurgan LA, Pedrycz W. A novel framework for imputation of missing values in databases. IEEE Trans Syst Man Cybern A Syst Humans. 2007;37:692\u2013709.","journal-title":"IEEE Trans Syst Man Cybern A Syst Humans"},{"key":"9639_CR9","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.ins.2015.03.018","volume":"311","author":"HC Valdiviezo","year":"2015","unstructured":"Valdiviezo HC, Van Aelst S. Tree-based prediction on incomplete data using imputation or surrogate decisions. Inf Sci (Ny). 2015;311:163\u201381.","journal-title":"Inf. Sci. (Ny)"},{"key":"9639_CR10","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1007\/s10115-015-0822-y","volume":"46","author":"MG Rahman","year":"2016","unstructured":"Rahman MG, Islam MZ. Missing value imputation using a fuzzy clustering-based EM approach. Knowl Inf Syst. 2016;46:389\u2013422.","journal-title":"Knowl Inf Syst"},{"key":"9639_CR11","doi-asserted-by":"crossref","unstructured":"Chakrabortty A, Cai T. Efficient and adaptive linear regression in semi-supervised settings 2018","DOI":"10.1214\/17-AOS1594"},{"key":"9639_CR12","first-page":"1889","volume":"34","author":"N Karmitsa","year":"2020","unstructured":"Karmitsa N, Taheri S, Bagirov A, M\u00e4kinen P. Missing value imputation via clusterwise linear regression. IEEE Trans Knowl Data Eng. 2020;34:1889\u2013901.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9639_CR13","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1109\/TPAMI.2012.39","volume":"35","author":"J Liu","year":"2012","unstructured":"Liu J, Musialski P, Wonka P, Ye J. Tensor completion for estimating missing values in visual data. IEEE Trans Pattern Anal Mach Intell. 2012;35:208\u201320.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9639_CR14","doi-asserted-by":"publisher","first-page":"402","DOI":"10.4097\/kjae.2013.64.5.402","volume":"64","author":"H Kang","year":"2013","unstructured":"Kang H. The prevention and handling of the missing data. Korean J Anesthesiol. 2013;64:402\u20136.","journal-title":"Korean J Anesthesiol"},{"key":"9639_CR15","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s10115-017-1038-0","volume":"53","author":"B Saha","year":"2017","unstructured":"Saha B, Gupta S, Phung D, Venkatesh S. Effective sparse imputation of patient conditions in electronic medical records for emergency risk predictions. Knowl Inf Syst. 2017;53:179\u2013206.","journal-title":"Knowl Inf Syst"},{"key":"9639_CR16","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1093\/bioinformatics\/btr597","volume":"28","author":"DJ Stekhoven","year":"2012","unstructured":"Stekhoven DJ, B\u00fchlmann P. MissForest\u2014non-parametric missing value imputation for mixed-type data. Bioinformatics. 2012;28:112\u20138.","journal-title":"Bioinformatics"},{"key":"9639_CR17","doi-asserted-by":"publisher","first-page":"9341","DOI":"10.1007\/s12652-020-02649-w","volume":"12","author":"A Mahmoudi","year":"2021","unstructured":"Mahmoudi A, Deng X, Javed SA, Yuan J. Large-scale multiple criteria decision-making with missing values: project selection through TOPSIS-OPA. J Ambient Intell Humaniz Comput. 2021;12:9341\u201362.","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"9639_CR18","first-page":"6134736","volume":"2016","author":"S Saha","year":"2016","unstructured":"Saha S, Ghosh A, Seal DB, Dey KN. An improved fuzzy based missing value estimation in DNA microarray validated by gene ranking. Adv Fuzzy Syst. 2016;2016:6134736.","journal-title":"Adv Fuzzy Syst"},{"key":"9639_CR19","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s10115-011-0424-2","volume":"32","author":"J Luengo","year":"2012","unstructured":"Luengo J, Garc\u00eda S, Herrera F. On the choice of the best imputation methods for missing values considering three groups of classification methods. Knowl Inf Syst. 2012;32:77\u2013108.","journal-title":"Knowl Inf Syst"},{"key":"9639_CR20","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.asoc.2016.05.044","volume":"47","author":"G Folino","year":"2016","unstructured":"Folino G, Pisani FS. Evolving meta-ensemble of classifiers for handling incomplete and unbalanced datasets in the cyber security domain. Appl Soft Comput. 2016;47:179\u201390.","journal-title":"Appl Soft Comput"},{"key":"9639_CR21","doi-asserted-by":"publisher","first-page":"10033","DOI":"10.1007\/s00521-019-04535-9","volume":"32","author":"PS Raja","year":"2020","unstructured":"Raja PS, Sasirekha K, Thangavel K. A novel fuzzy rough clustering parameter-based missing value imputation. Neural Comput Appl. 2020;32:10033\u201350.","journal-title":"Neural Comput Appl"},{"key":"9639_CR22","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.ins.2013.01.021","volume":"233","author":"IB Aydilek","year":"2013","unstructured":"Aydilek IB, Arslan A. A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm. Inf Sci (Ny). 2013;233:25\u201335.","journal-title":"Inf Sci (Ny)"},{"key":"9639_CR23","doi-asserted-by":"crossref","unstructured":"Raja PS, Thangavel K . Soft clustering based missing value imputation,\u201d in Digital Connectivity\u2013Social Impact: 51st Annual Convention of the Computer Society of India, CSI 2016, Coimbatore, India, December 8\u20139, 2016, Proceedings 51,\u00a02016, pp. 119\u2013133.","DOI":"10.1007\/978-981-10-3274-5_10"},{"key":"9639_CR24","doi-asserted-by":"publisher","first-page":"2419","DOI":"10.1007\/s10115-019-01427-1","volume":"62","author":"S Nikfalazar","year":"2020","unstructured":"Nikfalazar S, Yeh C-H, Bedingfield S, Khorshidi HA. Missing data imputation using decision trees and fuzzy clustering with iterative learning. Knowl Inf Syst. 2020;62:2419\u201337.","journal-title":"Knowl Inf Syst"},{"issue":"13","key":"9639_CR25","doi-asserted-by":"publisher","first-page":"5621","DOI":"10.1016\/j.eswa.2015.02.050","volume":"42","author":"A Purwar","year":"2015","unstructured":"Purwar A, Singh SK. Hybrid prediction model with missing value imputation for medical data. Expert Syst Appl. 2015;42(13):5621\u201331.","journal-title":"Expert Syst Appl"},{"key":"9639_CR26","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.neucom.2016.08.044","volume":"218","author":"KJ Nishanth","year":"2016","unstructured":"Nishanth KJ, Ravi V. Probabilistic neural network based categorical data imputation. Neurocomputing. 2016;218:17\u201325.","journal-title":"Neurocomputing"},{"issue":"4","key":"9639_CR27","first-page":"1697","volume":"30","author":"W-Y Loh","year":"2020","unstructured":"Loh W-Y, Zhang Q, Zhang W, Zhou P. Missing data, imputation and regression trees. Stat Sin. 2020;30(4):1697\u2013722.","journal-title":"Stat Sin"},{"issue":"1","key":"9639_CR28","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1109\/TKDE.2010.99","volume":"23","author":"X Zhu","year":"2010","unstructured":"Zhu X, Zhang S, Jin Z, Zhang Z, Xu Z. Missing value estimation for mixed-attribute data sets. IEEE Trans Knowl Data Eng. 2010;23(1):110\u201321.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9639_CR29","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.neucom.2016.04.015","volume":"205","author":"M Amiri","year":"2016","unstructured":"Amiri M, Jensen R. Missing data imputation using fuzzy-rough methods. Neurocomputing. 2016;205:152\u201364.","journal-title":"Neurocomputing"},{"issue":"6","key":"9639_CR30","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1109\/TFUZZ.2016.2516562","volume":"24","author":"X Miao","year":"2016","unstructured":"Miao X, Gao Y, Chen G, Zheng B, Cui H. Processing incomplete k nearest neighbor search. IEEE Trans Fuzzy Syst. 2016;24(6):1349\u201363.","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"9639_CR31","doi-asserted-by":"crossref","unstructured":"Li D, Deogun J, Spaulding W, Shuart B. Towards missing data imputation: a study of fuzzy k-means clustering method,\u201d in Rough Sets and Current Trends in Computing: 4th International Conference, RSCTC 2004, Uppsala, Sweden, June 1\u20135, 2004. Proceedings 4, 2004, pp. 573\u2013579.","DOI":"10.1007\/978-3-540-25929-9_70"},{"key":"9639_CR32","first-page":"37","volume":"4","author":"D Li","year":"2005","unstructured":"Li D, Deogun J, Spaulding W, Shuart B. Dealing with missing data: algorithms based on fuzzy set and rough set theories. Trans Rough Sets. 2005;4:37\u201357.","journal-title":"Trans Rough Sets"},{"key":"9639_CR33","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1109\/3477.956035","volume":"31","author":"RJ Hathaway","year":"2001","unstructured":"Hathaway RJ, Bezdek JC. Fuzzy c-means clustering of incomplete data. IEEE Trans Syst Man Cybern Part B. 2001;31:735\u201344.","journal-title":"IEEE Trans Syst Man Cybern Part B"},{"key":"9639_CR34","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1007\/s11071-015-2372-y","volume":"83","author":"P Balasubramaniam","year":"2016","unstructured":"Balasubramaniam P, Ananthi VP. Segmentation of nutrient deficiency in incomplete crop images using intuitionistic fuzzy C-means clustering algorithm. Nonlinear Dyn. 2016;83:849\u201366.","journal-title":"Nonlinear Dyn"},{"key":"9639_CR35","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.knosys.2013.08.023","volume":"53","author":"MG Rahman","year":"2013","unstructured":"Rahman MG, Islam MZ. Missing value imputation using decision trees and decision forests by splitting and merging records: two novel techniques. Knowl-Based Syst. 2013;53:51\u201365.","journal-title":"Knowl-Based Syst"},{"key":"9639_CR36","doi-asserted-by":"publisher","first-page":"e202400100","DOI":"10.1002\/pmic.202400100","volume":"25","author":"Y Schumann","year":"2025","unstructured":"Schumann Y, Gocke A, Neumann JE. Computational methods for data integration and imputation of missing values in omics datasets. Proteomics. 2025;25:e202400100.","journal-title":"Proteomics"},{"issue":"5","key":"9639_CR37","doi-asserted-by":"publisher","first-page":"1396","DOI":"10.1109\/TFUZZ.2021.3058643","volume":"30","author":"D Li","year":"2021","unstructured":"Li D, Zhang H, Li T, Bouras A, Yu X, Wang T. Hybrid missing value imputation algorithms using fuzzy c-means and vaguely quantified rough set. IEEE Trans Fuzzy Syst. 2021;30(5):1396\u2013408.","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"9639_CR38","doi-asserted-by":"publisher","first-page":"2518","DOI":"10.1016\/j.procs.2024.04.237","volume":"235","author":"K Sethia","year":"2024","unstructured":"Sethia K, Singh J, Gosain A. Handling incomplete data using radial basis kernelized intuitionistic fuzzy C-means. Procedia Comput Sci. 2024;235:2518\u201328.","journal-title":"Procedia Comput Sci"},{"issue":"7","key":"9639_CR39","doi-asserted-by":"publisher","first-page":"1992","DOI":"10.3390\/s20071992","volume":"20","author":"J Huang","year":"2020","unstructured":"Huang J, Mao B, Bai Y, Zhang T, Miao C. An integrated fuzzy C-means method for missing data imputation using taxi GPS data. Sensors. 2020;20(7):1992.","journal-title":"Sensors"},{"issue":"23","key":"9639_CR40","doi-asserted-by":"publisher","first-page":"9193","DOI":"10.1073\/pnas.87.23.9193","volume":"87","author":"WH Wolberg","year":"1990","unstructured":"Wolberg WH, Mangasarian OL. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc Natl Acad Sci. 1990;87(23):9193\u20136.","journal-title":"Proc Natl Acad Sci"},{"issue":"1","key":"9639_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12885-017-3877-1","volume":"18","author":"M Patr\u00edcio","year":"2018","unstructured":"Patr\u00edcio M, et al. Using resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer. 2018;18(1):1\u20138.","journal-title":"BMC Cancer"},{"issue":"4","key":"9639_CR42","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/0031-3203(91)90074-F","volume":"24","author":"Z-Q Hong","year":"1991","unstructured":"Hong Z-Q, Yang J-Y. Optimal discriminant plane for a small number of samples and design method of classifier on the plane. Pattern Recognit. 1991;24(4):317\u201324.","journal-title":"Pattern Recognit"},{"issue":"10","key":"9639_CR43","doi-asserted-by":"publisher","first-page":"3120","DOI":"10.1166\/asl.2016.7980","volume":"22","author":"R Machmud","year":"2016","unstructured":"Machmud R, Wijaya A, et al. Behavior determinant based cervical cancer early detection with machine learning algorithm. Adv Sci Lett. 2016;22(10):3120\u20133.","journal-title":"Adv Sci Lett"},{"key":"9639_CR44","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.csda.2017.02.012","volume":"112","author":"JY Nancy","year":"2017","unstructured":"Nancy JY, Khanna NH, Arputharaj K. Imputing missing values in unevenly spaced clinical time series data to build an effective temporal classification framework. Comput Stat Data Anal. 2017;112:63\u201379.","journal-title":"Comput Stat Data Anal"},{"key":"9639_CR45","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/0098-3004(84)90020-7","volume":"10","author":"JC Bezdek","year":"1984","unstructured":"Bezdek JC, Ehrlich R, Full W. FCM: The fuzzy c-means clustering algorithm. Comput Geosci. 1984;10:2\u20133. https:\/\/doi.org\/10.1016\/0098-3004(84)90020-7.","journal-title":"Comput Geosci"},{"key":"9639_CR46","doi-asserted-by":"crossref","unstructured":"Bezdek JC. Pattern recognition with fuzzy objective function algorithms. 1981 doi: 10.1007\/978-1-4757-0450-1.","DOI":"10.1007\/978-1-4757-0450-1"},{"issue":"3","key":"9639_CR47","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1177\/1471082X0800900301","volume":"9","author":"H Goldstein","year":"2009","unstructured":"Goldstein H, Carpenter J, Kenward MG, Levin KA. Multilevel models with multivariate mixed response types. Stat Modell. 2009;9(3):173\u201397.","journal-title":"Stat Modell"},{"key":"9639_CR48","doi-asserted-by":"publisher","first-page":"105122","DOI":"10.1016\/j.cmpb.2019.105122","volume":"184","author":"RK Bania","year":"2020","unstructured":"Bania RK, Halder A. R-Ensembler: a greedy rough set based ensemble attribute selection algorithm with kNN imputation for classification of medical data. Comput Methods Programs Biomed. 2020;184:105122.","journal-title":"Comput Methods Programs Biomed"},{"issue":"11","key":"9639_CR49","doi-asserted-by":"publisher","first-page":"2541","DOI":"10.1016\/j.jss.2012.05.073","volume":"85","author":"S Zhang","year":"2012","unstructured":"Zhang S. Nearest neighbor selection for iteratively kNN imputation. J Syst Softw. 2012;85(11):2541\u201352.","journal-title":"J Syst Softw"},{"issue":"6","key":"9639_CR50","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1093\/bioinformatics\/17.6.520","volume":"17","author":"O Troyanskaya","year":"2001","unstructured":"Troyanskaya O, et al. Missing value estimation methods for DNA microarrays. Bioinformatics. 2001;17(6):520\u20135.","journal-title":"Bioinformatics"},{"issue":"2","key":"9639_CR51","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1093\/bioinformatics\/bth499","volume":"21","author":"H Kim","year":"2005","unstructured":"Kim H, Golub GH, Park H. Missing value estimation for DNA microarray gene expression data: local least squares imputation. Bioinformatics. 2005;21(2):187\u201398.","journal-title":"Bioinformatics"},{"key":"9639_CR52","unstructured":"Rahman MG, Islam MZ. A decision tree-based missing value imputation technique for data pre-processing. in The 9th Australasian Data Mining Conference: AusDM 2011, 2011, pp. 41\u201350."},{"key":"9639_CR53","doi-asserted-by":"crossref","unstructured":"Little RJA, Rubin DB. Statistical analysis with missing data, vol. 793. Wiley 2019","DOI":"10.1002\/9781119482260"},{"issue":"1","key":"9639_CR54","first-page":"1","volume":"6","author":"P Schmitt","year":"2015","unstructured":"Schmitt P, Mandel J, Guedj M. A comparison of six methods for missing data imputation. J Biometrics Biostat. 2015;6(1):1.","journal-title":"J Biometrics Biostat"},{"issue":"3","key":"9639_CR55","first-page":"197","volume":"16","author":"L Beretta","year":"2016","unstructured":"Beretta L, Santaniello A. Nearest neighbor imputation algorithms: a critical evaluation. BMC Med Inform Decis Mak. 2016;16(3):197\u2013208.","journal-title":"BMC Med Inform Decis Mak"},{"key":"9639_CR56","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s001800200103","volume":"17","author":"H Toutenburg","year":"2002","unstructured":"Toutenburg H, Nittner T. Linear regression models with incomplete categorical covariates. Comput Stat. 2002;17:215\u201332.","journal-title":"Comput Stat"},{"key":"9639_CR57","doi-asserted-by":"crossref","unstructured":"Hwang S, Oh J, Cox J, Tang SJ, Tibbals HF. Blood detection in wireless capsule endoscopy using expectation maximization clustering. in Medical Imaging 2006: Image Processing, 2006, vol. 6144, pp. 577\u2013587.","DOI":"10.1117\/12.654109"},{"key":"9639_CR58","doi-asserted-by":"crossref","unstructured":"Nikfalazar S, Yeh CH, Bedingfield S, Khorshidi HA. A new iterative fuzzy clustering algorithm for multiple imputation of missing data,\u201d in 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017, pp. 1\u20136.","DOI":"10.1109\/FUZZ-IEEE.2017.8015560"},{"key":"9639_CR59","doi-asserted-by":"publisher","first-page":"107230","DOI":"10.1016\/j.compeleceng.2021.107230","volume":"93","author":"H Khan","year":"2021","unstructured":"Khan H, Wang X, Liu H. Missing value imputation through shorter interval selection driven by fuzzy C-means clustering. Comput Electr Eng. 2021;93:107230.","journal-title":"Comput Electr Eng"},{"issue":"1","key":"9639_CR60","first-page":"91","volume":"23","author":"S Goel","year":"2020","unstructured":"Goel S, Tushir M. A new iterative fuzzy clustering approach for incomplete data. J Stat Manag Syst. 2020;23(1):91\u2013102.","journal-title":"J Stat Manag Syst"},{"key":"9639_CR61","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2024.3466175","author":"Y Li","year":"2024","unstructured":"Li Y, Du M, Zhang W, Jiang X, Dong Y. Feature weighting-based deep fuzzy C-means for clustering incomplete time series. IEEE Trans Fuzzy Syst. 2024. https:\/\/doi.org\/10.1109\/TFUZZ.2024.3466175.","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"9639_CR62","doi-asserted-by":"publisher","first-page":"120065","DOI":"10.1016\/j.ins.2023.120065","volume":"659","author":"Z Zhang","year":"2024","unstructured":"Zhang Z, Yan X, Zhang L, Lai X, Lu W. Fuzzy neuron modeling of incomplete data for missing value imputation. Inf Sci (Ny). 2024;659:120065.","journal-title":"Inf Sci (Ny)"},{"issue":"1","key":"9639_CR63","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1016\/j.jksuci.2022.12.011","volume":"35","author":"A Ali","year":"2023","unstructured":"Ali A, Abu-Elkheir M, Atwan A, Elmogy M. Missing values imputation using fuzzy K-top matching value. J King Saud Univ Inf Sci. 2023;35(1):426\u201337.","journal-title":"J King Saud Univ Inf Sci"},{"key":"9639_CR64","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.matcom.2025.02.012","volume":"233","author":"H Zhang","year":"2025","unstructured":"Zhang H, Huang S-L. Improved fuzzy C-means clustering algorithm based on fuzzy particle swarm optimization for solving data clustering problems. Math Comput Simul. 2025;233:311\u201329. https:\/\/doi.org\/10.1016\/j.matcom.2025.02.012.","journal-title":"Math Comput Simul"},{"issue":"1","key":"9639_CR65","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/s44176-023-00022-7","volume":"2","author":"KK Mohanta","year":"2023","unstructured":"Mohanta KK, Sharanappa DS. A novel technique for solving intuitionistic fuzzy DEA model: an application in indian agriculture sector. Manag Syst Eng. 2023;2(1):12.","journal-title":"Manag Syst Eng"},{"key":"9639_CR66","doi-asserted-by":"publisher","first-page":"100357","DOI":"10.1016\/j.health.2024.100357","volume":"6","author":"V Chaubey","year":"2024","unstructured":"Chaubey V, Sharanappa DS, Mohanta KK, Verma R. A Malmquist fuzzy data envelopment analysis model for performance evaluation of rural healthcare systems. Healthc Anal. 2024;6:100357.","journal-title":"Healthc Anal"},{"issue":"14","key":"9639_CR67","doi-asserted-by":"publisher","first-page":"9575","DOI":"10.1007\/s00500-021-05739-9","volume":"25","author":"R Verma","year":"2021","unstructured":"Verma R. Fuzzy MABAC method based on new exponential fuzzy information measures. Soft Comput. 2021;25(14):9575\u201389.","journal-title":"Soft Comput"},{"key":"9639_CR68","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.aej.2024.11.037","volume":"113","author":"N Ma","year":"2025","unstructured":"Ma N, Wu K, Yuan Y, Li J, Wu X. PMWFCM: a possibility based MultiKernel weighted fuzzy clustering algorithm for classification of driving behaviors. Alexandria Eng J. 2025;113:249\u201361.","journal-title":"Alexandria Eng J"},{"key":"9639_CR69","doi-asserted-by":"publisher","first-page":"278","DOI":"10.4028\/www.scientific.net\/MSF.803.278","volume":"803","author":"NM Noor","year":"2015","unstructured":"Noor NM, Al Bakri Abdullah MM, Yahaya AS, Ramli NA. Comparison of linear interpolation method and mean method to replace the missing values in environmental data set. Mater Sci Forum. 2015;803:278\u201381.","journal-title":"Mater Sci Forum"},{"key":"9639_CR70","first-page":"65","volume":"11","author":"P Kaur","year":"2012","unstructured":"Kaur P, Soni AK, Gosain A. Novel intuitionistic fuzzy c-means clustering for linearly and nonlinearly separable data. WSEAS Trans Comput. 2012;11:65\u201376.","journal-title":"WSEAS Trans Comput"},{"issue":"5","key":"9639_CR71","doi-asserted-by":"publisher","first-page":"1445","DOI":"10.1109\/TFUZZ.2022.3203506","volume":"31","author":"W Zhang","year":"2022","unstructured":"Zhang W, Deng Z, Choi K-S, Wang S. End-to-end incomplete multiview fuzzy clustering with adaptive missing view imputation and cooperative learning. IEEE Trans Fuzzy Syst. 2022;31(5):1445\u201359.","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"1","key":"9639_CR72","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/s12874-025-02496-3","volume":"25","author":"M Afkanpour","year":"2025","unstructured":"Afkanpour M, Tehrany Dehkordy D, Momeni M, Tabesh H. Conceptual framework as a guide to choose appropriate imputation method for missing values in a clinical structured dataset. BMC Med Res Methodol. 2025;25(1):43.","journal-title":"BMC Med Res Methodol"},{"key":"9639_CR73","doi-asserted-by":"publisher","first-page":"100063","DOI":"10.1016\/j.csa.2024.100063","volume":"3","author":"M Tahir","year":"2025","unstructured":"Tahir M, Abdullah A, Udzir NI, Kasmiran KA. A novel approach for handling missing data to enhance network intrusion detection system. Cyber Secur Appl. 2025;3:100063.","journal-title":"Cyber Secur Appl"},{"key":"9639_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2010.05.005","author":"T Chaira","year":"2011","unstructured":"Chaira T. A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images. Appl Soft Comput J. 2011. https:\/\/doi.org\/10.1016\/j.asoc.2010.05.005.","journal-title":"Appl Soft Comput J"},{"key":"9639_CR75","doi-asserted-by":"crossref","unstructured":"Panda S, Sahu S, Jena P, Chattopadhyay S. Comparing fuzzy-C means and K-means clustering techniques: a comprehensive study,\u201d in Advances in Computer Science, Engineering \\& Applications: Proceedings of the Second International Conference on Computer Science, Engineering and Applications (ICCSEA 2012), May 25\u201327, 2012, New Delhi, India, Volume 1, 2012, pp. 451\u2013460.","DOI":"10.1007\/978-3-642-30157-5_45"},{"key":"9639_CR76","doi-asserted-by":"crossref","unstructured":"Peters G, Crespo F. An illustrative comparison of rough k-means to classical clustering approaches, in Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 14th International Conference, RSFDGrC 2013, Halifax, NS, Canada, October 11\u201314, 2013. Proceedings 14, 2013, pp. 337\u2013344.","DOI":"10.1007\/978-3-642-41218-9_36"},{"key":"9639_CR77","doi-asserted-by":"crossref","unstructured":"Vergani AA, Binaghi E. A soft davies-bouldin separation measure, in 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018, pp. 1\u20138.","DOI":"10.1109\/FUZZ-IEEE.2018.8491581"},{"issue":"1","key":"9639_CR78","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1214\/aoms\/1177731944","volume":"11","author":"M Friedman","year":"1940","unstructured":"Friedman M. A comparison of alternative tests of significance for the problem of m rankings. Ann Math Stat. 1940;11(1):86\u201392.","journal-title":"Ann Math Stat"}],"container-title":["Discover Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09639-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10791-025-09639-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09639-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T10:05:36Z","timestamp":1751364336000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10791-025-09639-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":78,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["9639"],"URL":"https:\/\/doi.org\/10.1007\/s10791-025-09639-6","relation":{},"ISSN":["2948-2992"],"issn-type":[{"value":"2948-2992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,1]]},"assertion":[{"value":"16 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"133"}}