iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://api.crossref.org/works/10.1016/J.ASOC.2021.107922
{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T12:01:43Z","timestamp":1726920103524},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["311164\/2020-0"],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006162","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Ci\u00eancia e Tecnologia do Estado de Pernambuco","doi-asserted-by":"publisher","award":["PBPG-0402-1.03\/17"],"id":[{"id":"10.13039\/501100006162","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1016\/j.asoc.2021.107922","type":"journal-article","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T21:39:22Z","timestamp":1632951562000},"page":"107922","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"special_numbering":"PA","title":["Fuzzy clustering algorithms with distance metric learning and entropy regularization"],"prefix":"10.1016","volume":"113","author":[{"given":"Sara I.R.","family":"Rodr\u00edguez","sequence":"first","affiliation":[]},{"given":"Francisco de A.T.","family":"de Carvalho","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.asoc.2021.107922_b1","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1109\/TFUZZ.2012.2201485","article-title":"Fuzzy C-means algorithms for very large data","volume":"20","author":"Havens","year":"2012","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"3","key":"10.1016\/j.asoc.2021.107922_b2","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1109\/TFUZZ.2011.2179659","article-title":"A generalization of distance functions for fuzzy C-means clustering with centroids of arithmetic means","volume":"20","author":"Wu","year":"2011","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.asoc.2021.107922_b3","series-title":"Data Clustering: Theory, Algorithms, and Applications","author":"Gan","year":"2020"},{"key":"10.1016\/j.asoc.2021.107922_b4","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.asoc.2016.08.020","article-title":"Soft fuzzy rough set-based MR brain image segmentation","volume":"54","author":"Namburu","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2021.107922_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2019.105928","article-title":"Fuzzy C-means clustering through SSIM and patch for image segmentation","volume":"87","author":"Tang","year":"2020","journal-title":"Appl. Soft Comput."},{"issue":"7","key":"10.1016\/j.asoc.2021.107922_b6","first-page":"10490","article-title":"Comparative study of different clustering algorithms","volume":"3","author":"Patil","year":"2014","journal-title":"Int. J. Adv. Res. Electr. Electron. Instrum. Eng."},{"issue":"3","key":"10.1016\/j.asoc.2021.107922_b7","first-page":"169","article-title":"Review on fuzzy clustering algorithms","volume":"2","author":"Ali","year":"2008","journal-title":"J. Adv. Comput."},{"key":"10.1016\/j.asoc.2021.107922_b8","series-title":"Finding Groups in Data: An Introduction To Cluster Analysis","author":"Kaufman","year":"2009"},{"issue":"5","key":"10.1016\/j.asoc.2021.107922_b9","first-page":"382","article-title":"Using fuzzy C-means clustering based on integration of psychological and physiological data for therapeutic music design","volume":"34","author":"Chiu","year":"2017","journal-title":"J. Ind. Prod. Eng."},{"key":"10.1016\/j.asoc.2021.107922_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2020.110230","article-title":"Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries","volume":"140","author":"Mahmoudi","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"10.1016\/j.asoc.2021.107922_b11","first-page":"133","article-title":"Fuzzy C-means (FCM) clustering algorithm: a decade review from 2000 to 2014","volume":"2","author":"Nayak","year":"2015","journal-title":"Comput. Intell. Data Min."},{"issue":"1","key":"10.1016\/j.asoc.2021.107922_b12","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MCI.2018.2881643","article-title":"Fuzzy clustering: A historical perspective","volume":"14","author":"Ruspini","year":"2019","journal-title":"IEEE Comput. Intell. Mag."},{"key":"10.1016\/j.asoc.2021.107922_b13","series-title":"Pattern Recognition with Fuzzy Objective Function Algorithms","author":"Bezdek","year":"2013"},{"key":"10.1016\/j.asoc.2021.107922_b14","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.knosys.2018.12.007","article-title":"Density-sensitive fuzzy kernel maximum entropy clustering algorithm","volume":"166","author":"Tao","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.asoc.2021.107922_b15","series-title":"Proceedings of 1995 IEEE International Conference on Fuzzy Systems","first-page":"2227","article-title":"A maximum-entropy approach to fuzzy clustering","volume":"4","author":"Li","year":"1995"},{"key":"10.1016\/j.asoc.2021.107922_b16","unstructured":"M. Sadaaki, M. Masao, Fuzzy C-Means as a regularization and maximum entropy approach, in: Proceedings of the 7th International Fuzzy Systems Association World Congress (IFSA\u201997), vol. 2, 1997, 86\u201392."},{"issue":"6","key":"10.1016\/j.asoc.2021.107922_b17","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1016\/j.csda.2005.01.008","article-title":"Fuzzy unsupervised classification of multivariate time trajectories with the Shannon entropy regularization","volume":"50","author":"Coppi","year":"2006","journal-title":"Comput. Statist. Data Anal."},{"key":"10.1016\/j.asoc.2021.107922_b18","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.ins.2016.01.101","article-title":"A survey on soft subspace clustering","volume":"348","author":"Deng","year":"2016","journal-title":"Inform. Sci."},{"key":"10.1016\/j.asoc.2021.107922_b19","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.asoc.2013.03.002","article-title":"Evolving soft subspace clustering","volume":"14","author":"Zhu","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2021.107922_b20","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.patcog.2015.10.018","article-title":"Distance metric learning for soft subspace clustering in composite kernel space","volume":"52","author":"Wang","year":"2016","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.asoc.2021.107922_b21","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.patcog.2015.09.027","article-title":"Soft subspace clustering of categorical data with probabilistic distance","volume":"51","author":"Chen","year":"2016","journal-title":"Pattern Recognit."},{"issue":"10","key":"10.1016\/j.asoc.2021.107922_b22","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1016\/j.patrec.2004.03.008","article-title":"Improving fuzzy C-means clustering based on feature-weight learning","volume":"25","author":"Wang","year":"2004","journal-title":"Pattern Recognit. Lett."},{"issue":"8","key":"10.1016\/j.asoc.2021.107922_b23","doi-asserted-by":"crossref","first-page":"4798","DOI":"10.1016\/j.asoc.2011.07.002","article-title":"Eew-SC: Enhanced entropy-weighting subspace clustering for high dimensional gene expression data clustering analysis","volume":"11","author":"Deng","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2021.107922_b24","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.neucom.2012.09.043","article-title":"Color segmentation by fuzzy co-clustering of chrominance color features","volume":"120","author":"Hanmandlu","year":"2013","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2021.107922_b25","series-title":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","first-page":"1","article-title":"Fuzzy clustering algorithm with automatic variable selection and entropy regularization","author":"Rodr\u00edguez","year":"2017"},{"key":"10.1016\/j.asoc.2021.107922_b26","series-title":"2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","first-page":"1","article-title":"Fuzzy clustering algorithm based on adaptive city-block distance and entropy regularization","author":"Rodr\u00edguez","year":"2018"},{"issue":"3","key":"10.1016\/j.asoc.2021.107922_b27","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/j.patcog.2009.09.010","article-title":"Enhanced soft subspace clustering integrating within-cluster and between-cluster information","volume":"43","author":"Deng","year":"2010","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.asoc.2021.107922_b28","series-title":"1978 IEEE Conference on Decision and Control Including the 17th Symposium on Adaptive Processes","first-page":"761","article-title":"Fuzzy clustering with a fuzzy covariance matrix","author":"Gustafson","year":"1979"},{"issue":"5","key":"10.1016\/j.asoc.2021.107922_b29","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1109\/TPAMI.2005.95","article-title":"Automated variable weighting in K-means type clustering","volume":"27","author":"Huang","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10.1016\/j.asoc.2021.107922_b30","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/0165-0114(91)90064-W","article-title":"L1-norm based fuzzy clustering","volume":"39","author":"Jajuga","year":"1991","journal-title":"Fuzzy Sets and Systems"},{"issue":"2","key":"10.1016\/j.asoc.2021.107922_b31","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/S0165-0114(97)00126-7","article-title":"Gaussian clustering method based on maximum-fuzzy-entropy interpretation","volume":"102","author":"Li","year":"1999","journal-title":"Fuzzy Sets and Systems"},{"key":"10.1016\/j.asoc.2021.107922_b32","series-title":"Digital Pattern Recognition","first-page":"47","article-title":"Clustering analysis","author":"Diday","year":"1976"},{"issue":"22","key":"10.1016\/j.asoc.2021.107922_b33","doi-asserted-by":"crossref","first-page":"2841","DOI":"10.1093\/bioinformatics\/btq534","article-title":"A simple and fast method to determine the parameters for fuzzy C-means cluster analysis","volume":"26","author":"Schw\u00e4mmle","year":"2010","journal-title":"Bioinformatics"},{"issue":"3","key":"10.1016\/j.asoc.2021.107922_b34","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1109\/TFUZZ.2011.2179303","article-title":"Comparing fuzzy partitions: A generalization of the rand index and related measures","volume":"20","author":"Hullermeier","year":"2011","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"1","key":"10.1016\/j.asoc.2021.107922_b35","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","article-title":"Comparing partitions","volume":"2","author":"Hubert","year":"1985","journal-title":"J. Classification"},{"issue":"21","key":"10.1016\/j.asoc.2021.107922_b36","doi-asserted-by":"crossref","first-page":"2833","DOI":"10.1016\/j.fss.2006.06.004","article-title":"Partitional fuzzy clustering methods based on adaptive quadratic distances","volume":"157","author":"de\u00a0Carvalho","year":"2006","journal-title":"Fuzzy Sets and Systems"},{"issue":"200","key":"10.1016\/j.asoc.2021.107922_b37","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","article-title":"The use of ranks to avoid the assumption of normality implicit in the analysis of variance","volume":"32","author":"Friedman","year":"1937","journal-title":"J. Amer. Statist. Assoc."},{"key":"10.1016\/j.asoc.2021.107922_b38","series-title":"Distribution-free multiple comparison","author":"Nemenyi","year":"1963"},{"issue":"1","key":"10.1016\/j.asoc.2021.107922_b39","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s11634-014-0169-3","article-title":"Trimmed fuzzy clustering for interval-valued data","volume":"9","author":"D\u03a9\u00a0A\u00c1Urso","year":"2015","journal-title":"Advances in Data Analysis and Classification"},{"key":"10.1016\/j.asoc.2021.107922_b40","series-title":"UCI machine learning repository","author":"Bache","year":"2013"},{"issue":"9","key":"10.1016\/j.asoc.2021.107922_b41","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1016\/0031-3203(93)90135-J","article-title":"A review on image segmentation techniques","volume":"26","author":"Pal","year":"1993","journal-title":"Pattern Recognit."},{"issue":"2","key":"10.1016\/j.asoc.2021.107922_b42","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s40009-016-0513-6","article-title":"Efficient combination of texture and color features in a new spectral clustering method for PolSAR image segmentation","volume":"40","author":"Akbarizadeh","year":"2017","journal-title":"National Acad. Sci. Lett."},{"key":"10.1016\/j.asoc.2021.107922_b43","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.knosys.2017.05.018","article-title":"Knowledge-leveraged transfer fuzzy C-means for texture image segmentation with self-adaptive cluster prototype matching","volume":"130","author":"Qian","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.asoc.2021.107922_b44","series-title":"2018 14th International Conference on Computational Intelligence and Security (CIS)","first-page":"135","article-title":"Multi-feature fusion method applied in texture image segmentation","author":"Du","year":"2018"},{"key":"10.1016\/j.asoc.2021.107922_b45","unstructured":"T. Randen, Brodatz Texture, [Online; accessed July, 2021], http:\/\/www.ux.uis.no\/~tranden\/brodatz.html."},{"issue":"3","key":"10.1016\/j.asoc.2021.107922_b46","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.patrec.2003.10.008","article-title":"Simple Gabor feature space for invariant object recognition","volume":"25","author":"Kyrki","year":"2004","journal-title":"Pattern Recognit. Lett."}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494621008449?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494621008449?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,4,15]],"date-time":"2023-04-15T12:21:27Z","timestamp":1681561287000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494621008449"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12]]},"references-count":46,"alternative-id":["S1568494621008449"],"URL":"http:\/\/dx.doi.org\/10.1016\/j.asoc.2021.107922","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2021,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Fuzzy clustering algorithms with distance metric learning and entropy regularization","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2021.107922","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"107922"}}