{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T12:36:35Z","timestamp":1761395795849,"version":"3.37.3"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2020,7,18]],"date-time":"2020-07-18T00:00:00Z","timestamp":1595030400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,18]],"date-time":"2020-07-18T00:00:00Z","timestamp":1595030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No.61876101","61802234 and 61806114"],"award-info":[{"award-number":["No.61876101","61802234 and 61806114"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Social Science Fund Project of Shandong","award":["16BGLJ06","11CGLJ22"],"award-info":[{"award-number":["16BGLJ06","11CGLJ22"]}]},{"name":"China Postdoctoral Science Foundation Funded Project","award":["2017M612339","2018M642695"],"award-info":[{"award-number":["2017M612339","2018M642695"]}]},{"name":"Natural Science Foundation of the Shandong Provincial","award":["ZR2019QF007"],"award-info":[{"award-number":["ZR2019QF007"]}]},{"name":"China Postdoctoral Special Funding Project","award":["2019T120607"],"award-info":[{"award-number":["2019T120607"]}]},{"name":"Youth Fund for Humanities and Social Sciences, Ministry of Education","award":["19YJCZH244"],"award-info":[{"award-number":["19YJCZH244"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1007\/s10489-020-01784-3","type":"journal-article","created":{"date-parts":[[2020,7,18]],"date-time":"2020-07-18T05:19:10Z","timestamp":1595049550000},"page":"4378-4393","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Novel coupled DP system for fuzzy C-means clustering and image segmentation"],"prefix":"10.1007","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4119-0042","authenticated-orcid":false,"given":"Zhenni","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Xiyu","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,18]]},"reference":[{"key":"1784_CR1","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-0450-1","volume-title":"Pattern recognition with fuzzy objective function algorithms","author":"JC Bezdek","year":"1981","unstructured":"Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York"},{"issue":"8","key":"1784_CR2","first-page":"1237C1256","volume":"180","author":"S Das","year":"2010","unstructured":"Das S, Sil S (2010) Kernel-induced fuzzy clustering of image pixels with an improved different evolution algorithm. Inf Sci 180(8):1237C1256","journal-title":"Inf Sci"},{"issue":"part B","key":"1784_CR3","first-page":"946","volume":"14","author":"BA Pimentel","year":"2016","unstructured":"Pimentel BA, Souza RMCRD (2016) Multivariate fuzzy C-means algorithms with weighting. Neurocomputing, 2015 14(part B):946\u2013965","journal-title":"Neurocomputing, 2015"},{"key":"1784_CR4","doi-asserted-by":"publisher","unstructured":"Lei T, Liu P, Jia XH, Zhang XD, Meng HY, Nandi AK (2019) Automatic fuzzy clustering framework for image segmentation. IEEE Trans Fuzzy Sys, https:\/\/doi.org\/10.1109\/TFUZZ.2019.2930030","DOI":"10.1109\/TFUZZ.2019.2930030"},{"key":"1784_CR5","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1016\/j.ins.2018.05.053","volume":"507","author":"J Zhou","year":"2020","unstructured":"Zhou J, Lai ZH, Miao DQ, Gao C, Yue XD (2020) Multigranulation rough-fuzzy clustering based on shadowed sets. Inf Sci 507:553\u2013573","journal-title":"Inf Sci"},{"key":"1784_CR6","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.patrec.2019.02.017","volume":"122","author":"H Verma","year":"2019","unstructured":"Verma H, Gupta A, Kumar D (2019) A modified intuitionistic fuzzy c-means algorithm incorporating hesitation degree. Pattern Recognit Lett 122:45\u201352","journal-title":"Pattern Recognit Lett"},{"key":"1784_CR7","doi-asserted-by":"publisher","unstructured":"Memon KH, Lee DH Generalised kernel weighted fuzzy C-means clustering algorithm with local information, Fuzzy Set System, to be published, https:\/\/doi.org\/10.1016\/j.fss.2018.01.019","DOI":"10.1016\/j.fss.2018.01.019"},{"key":"1784_CR8","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.knosys.2017.03.024","volume":"125","author":"H Peng","year":"2017","unstructured":"Peng H, Shi P, Wang J, Agustn RN, Mario J (2017) Multi-objective fuzzy clustering approach based on tissue-like membrane systems. Knowl Based Syst 125:74\u201382","journal-title":"Knowl Based Syst"},{"key":"1784_CR9","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.neucom.2015.01.106","volume":"188","author":"Y Ding","year":"2016","unstructured":"Ding Y, Fu X (2016) Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm. Neurocomputing 188:233\u2013238","journal-title":"Neurocomputing"},{"issue":"11","key":"1784_CR10","doi-asserted-by":"crossref","first-page":"578","DOI":"10.3390\/e19110578","volume":"19","author":"WK Zang","year":"2017","unstructured":"Zang WK, Zhang WN, Zhang WQ, Liu XY (2017) A Kernel-based intuitionistic fuzzy C-means clustering using a DNA genetic algorithm for magnetic resonance image segmentation. Entropy 19(11):578","journal-title":"Entropy"},{"key":"1784_CR11","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.asoc.2018.01.003","volume":"65","author":"TP Xuan","year":"2018","unstructured":"Xuan TP, Siarry P, Oulhadj H (2018) Integrating fuzzy entropy clustering with an improved PSO for MRI brain image segmentation. Appl Soft Comput 65:230\u2013242","journal-title":"Appl Soft Comput"},{"issue":"3","key":"1784_CR12","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1007\/s10489-017-0917-0","volume":"47","author":"R Gupta","year":"2017","unstructured":"Gupta R, Muttoo SK, Pal SK (2017) Fuzzy C-means clustering and particle swarm optimization based scheme for common service center location allocation. Appl Intell 47(3):624\u2013643","journal-title":"Appl Intell"},{"issue":"6","key":"1784_CR13","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.1007\/s00500-017-2899-6","volume":"23","author":"C Selvi","year":"2019","unstructured":"Selvi C, Sivasankar E (2019) A novel optimization algorithm for recommender system using modified fuzzy c-means clustering approach. Soft Comput 23(6):1901\u20131916","journal-title":"Soft Comput"},{"key":"1784_CR14","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.ijrmms.2018.10.030","volume":"113","author":"M Abbas","year":"2019","unstructured":"Abbas M, Beiki M (2019) Applying evolutionary optimization algorithms for improving fuzzy C-mean clustering performance to predict the deformation modulus of rock mass. Int J Rock Mech Min Sci 113:172\u2013182","journal-title":"Int J Rock Mech Min Sci"},{"issue":"3","key":"1784_CR15","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/s10489-016-0858-z","volume":"46","author":"VN Phu","year":"2017","unstructured":"Phu VN, Dat ND, Ngoc Tran VT, Ngoc Chau VT, Nguyen TN (2017) Fuzzy C-means for english sentiment classification in a distributed system. Appl Intell 46(3):717\u2013738","journal-title":"Appl Intell"},{"key":"1784_CR16","doi-asserted-by":"publisher","unstructured":"Ali AR, Couceiro MS, Anter AM, Hassanian AE (2014) Evaluating an evolutionary particle swarm optimization for fast fuzzy C-means clustering on liver CT images computer vision and image processing in intelligent systems and multimedia technologies, pp 21, DOI: https:\/\/doi.org\/10.4018\/978-1-4666-6030-4.ch001","DOI":"10.4018\/978-1-4666-6030-4.ch001"},{"issue":"99","key":"1784_CR17","first-page":"1","volume":"PP","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 Fuzz Syst PP(99):1\u20131","journal-title":"IEEE Trans Fuzz Syst"},{"issue":"2","key":"1784_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1352789.1352792","volume":"3","author":"S Garruzzo","year":"2008","unstructured":"Garruzzo S, Rosaci D (2008) Agent clustering based on semantic negotiation. ACM Trans Auton Adapt Syst (ACM TAAS) 3(2):1\u201339","journal-title":"ACM Trans Auton Adapt Syst (ACM TAAS)"},{"issue":"7","key":"1784_CR19","doi-asserted-by":"publisher","first-page":"3682","DOI":"10.1016\/j.eswa.2014.12.042","volume":"42","author":"NT Thong","year":"2015","unstructured":"Thong NT, Son LH (2015) HIFCF: an Effective hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis. Expert Syst Appl 42(7):3682\u20133701","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1784_CR20","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1006\/jcss.1999.1693","volume":"61","author":"G Pa\u030cun","year":"2008","unstructured":"Pa\u030cun G (2008) Computing with membranes. J Comput Syst Sci 61(1):108\u2013143","journal-title":"J Comput Syst Sci"},{"issue":"2","key":"1784_CR21","doi-asserted-by":"crossref","first-page":"208","DOI":"10.3724\/SP.J.1016.2010.00208","volume":"33","author":"GX Zhang","year":"2010","unstructured":"Zhang GX, Pan LQ (2010) A survey of membrane computing as a new branch of natural computing. Chin J Comput 33(2):208\u2013214","journal-title":"Chin J Comput"},{"issue":"9","key":"1784_CR22","doi-asserted-by":"crossref","first-page":"e0162882","DOI":"10.1371\/journal.pone.0162882","volume":"11","author":"YZ Zhao","year":"2016","unstructured":"Zhao YZ, Liu XY, Wang WP (2016) Spiking neural P systems with neuron division and dissolution. PLOS ONE 11(9 ):e0162882","journal-title":"PLOS ONE"},{"key":"1784_CR23","first-page":"1","volume":"2017","author":"XY Liu","year":"2017","unstructured":"Liu XY, Zhao YZ, Sun MH (2017) An improved apriori algorithm based on an evolution-communication tissue-like P system with promoters and inhibitors. Discret Dyn Nat Soc 2017:1\u201311","journal-title":"Discret Dyn Nat Soc"},{"issue":"8","key":"1784_CR24","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1109\/TNB.2015.2503603","volume":"14","author":"T Song","year":"2015","unstructured":"Song T, Xu J, Pan LQ (2015) On the universality and non-universality of spiking neural P systems with rules on synapses. IEEE Trans Nanobiosci 14(8):960\u2013966","journal-title":"IEEE Trans Nanobiosci"},{"key":"1784_CR25","first-page":"1","volume":"2017","author":"XY Liu","year":"2017","unstructured":"Liu XY, Xue J (2017) A cluster splitting technique by hopfield networks and P systems on simplices. Neural Process Lett 2017:1\u201324","journal-title":"Neural Process Lett"},{"key":"1784_CR26","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.knosys.2017.03.024","volume":"125","author":"H Peng","year":"2017","unstructured":"Peng H, Shi P, Wang J, Agustn RN, Mario J (2017) Multiobjective fuzzy clustering approach based on tissue-like membrane systems. Knowl Based Syst 125:74\u201382","journal-title":"Knowl Based Syst"},{"issue":"5","key":"1784_CR27","doi-asserted-by":"crossref","first-page":"3652","DOI":"10.1166\/jctn.2016.5196","volume":"13","author":"YZ Zhao","year":"2016","unstructured":"Zhao YZ, Liu XY, Yan XB (2016) A grid-based chameleon algorithm based on the tissue-like P system with promoters and inhibitors. J Comput Theoret Nanosci 13(5):3652\u20133658","journal-title":"J Comput Theoret Nanosci"},{"issue":"12","key":"1784_CR28","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1016\/j.compchemeng.2007.01.012","volume":"31","author":"JL Tao","year":"2007","unstructured":"Tao JL, Wang N (2007) DNA computing based RNA genetic algorithm with applications in parameter estimation of chemical engineering processes. Comput Chem Eng 31(12):1602\u20131618","journal-title":"Comput Chem Eng"},{"issue":"3","key":"1784_CR29","doi-asserted-by":"crossref","first-page":"76","DOI":"10.3390\/a10030076","volume":"10","author":"WK Zang","year":"2017","unstructured":"Zang WK, Zhang WN, Zhang WQ, Liu XY (2017) A genetic algorithm using triplet nucleotide encoding and DNA reproduction operations for unconstrained optimization problems. Algorithms 10(3):76","journal-title":"Algorithms"},{"issue":"8","key":"1784_CR30","doi-asserted-by":"crossref","first-page":"1750023","DOI":"10.1142\/S0218001417500239","volume":"31","author":"WK Zang","year":"2017","unstructured":"Zang WK, Ren LY, Zhang WQ, Liu XY (2017) Automatic density peaks clustering using DNA genetic algorithm optimized data field and gaussian process. Int J Patt Recogn Artif Intell 31(8):1750023","journal-title":"Int J Patt Recogn Artif Intell"},{"issue":"4","key":"1784_CR31","doi-asserted-by":"crossref","first-page":"1750010","DOI":"10.1142\/S0218001417500100","volume":"31","author":"WK Zang","year":"2017","unstructured":"Zang WK, Jiang ZN, Ren LY (2017) Improved spectral clustering based on density combining DNA genetic algorithm. Int J Patt Recogn Artif Intell 31(4):1750010","journal-title":"Int J Patt Recogn Artif Intell"},{"issue":"6","key":"1784_CR32","doi-asserted-by":"crossref","first-page":"3763","DOI":"10.1166\/jctn.2016.5209","volume":"13","author":"WK Zang","year":"2016","unstructured":"Zang WK, Sun MH, Jiang ZN (2016) A DNA genetic algorithm inspired by biological membrane structure. J Comput Theoret Nanosci 13(6):3763\u20133772","journal-title":"J Comput Theoret Nanosci"},{"key":"1784_CR33","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.asoc.2015.01.031","volume":"30","author":"CL Yang","year":"2015","unstructured":"Yang CL, Kuo RJ, Chien CH, Quyen NTP (2015) Non-dominated sorting genetic algorithm using fuzzy membership chromosome for categorical data clustering. Appl Soft Comput 30:113\u2013122","journal-title":"Appl Soft Comput"},{"issue":"1","key":"1784_CR34","first-page":"100","volume":"28","author":"JA Hartigan","year":"1979","unstructured":"Hartigan JA, Wong MA (1979) Algorithm AS 136: a K-means clustering algorithm. J R Stat Soc C-Appl 28(1):100\u2013108","journal-title":"J R Stat Soc C-Appl"},{"issue":"2-3","key":"1784_CR35","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(2-3):191\u2013203","journal-title":"Comput Geosci"},{"issue":"3","key":"1784_CR36","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1023\/B:NEPL.0000011135.19145.1b","volume":"18","author":"DQ Zhang","year":"2003","unstructured":"Zhang DQ, Chen SC (2003) Clustering incomplete data using kernel-based fuzzy C-means algorithm. Neural Process Lett 18(3):155\u2013162","journal-title":"Neural Process Lett"},{"key":"1784_CR37","doi-asserted-by":"publisher","first-page":"100485","DOI":"10.1016\/j.swevo.2019.01.001","volume":"50","author":"RH Shang","year":"2019","unstructured":"Shang RH, Zhang WT, Li F, Jiao LC, Stolkin R (2019) Multi-objective artificial immune algorithm for fuzzy clustering based on multiple kernels. Swarm and Evolutionary Computation 50:100485","journal-title":"Swarm and Evolutionary Computation"},{"key":"1784_CR38","first-page":"2837","volume":"11","author":"NX Vinh","year":"2010","unstructured":"Vinh NX, Epps J, Bailey J (2010) Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J Mach Learn Res 11:2837\u20132854","journal-title":"J Mach Learn Res"},{"issue":"2","key":"1784_CR39","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.inffus.2006.05.006","volume":"9","author":"ZY He","year":"2005","unstructured":"He ZY, Xu XF, Deng SC (2005) K-ANMI: a mutual information based clustering algorithm for categorical data. Inform Fusion 9(2):223\u2013233","journal-title":"Inform Fusion"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01784-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-020-01784-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01784-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,17]],"date-time":"2021-07-17T23:21:17Z","timestamp":1626564077000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-020-01784-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,18]]},"references-count":39,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1784"],"URL":"https:\/\/doi.org\/10.1007\/s10489-020-01784-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2020,7,18]]},"assertion":[{"value":"18 July 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interests"}}]}}