{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:42:43Z","timestamp":1775608963887,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2020,3,26]],"date-time":"2020-03-26T00:00:00Z","timestamp":1585180800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,3,26]],"date-time":"2020-03-26T00:00:00Z","timestamp":1585180800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100014717","name":"National Outstanding Youth Science Fund Project of National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773268"],"award-info":[{"award-number":["61773268"]}],"id":[{"id":"10.13039\/100014717","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014717","name":"National Outstanding Youth Science Fund Project of National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61502177"],"award-info":[{"award-number":["61502177"]}],"id":[{"id":"10.13039\/100014717","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1007\/s11263-020-01320-3","type":"journal-article","created":{"date-parts":[[2020,3,26]],"date-time":"2020-03-26T06:02:46Z","timestamp":1585202566000},"page":"1982-1995","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Enhanced Balanced Min Cut"],"prefix":"10.1007","volume":"128","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2818-4652","authenticated-orcid":false,"given":"Xiaojun","family":"Chen","sequence":"first","affiliation":[]},{"given":"Weijun","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Feiping","family":"Nie","sequence":"additional","affiliation":[]},{"given":"Joshua Zhexue","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Li","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,3,26]]},"reference":[{"key":"1320_CR1","first-page":"1409","volume":"7","author":"TD Bie","year":"2006","unstructured":"Bie, T. D., & Cristianini, N. (2006). Fast SDP relaxations of graph cut clustering, transduction, and other combinatorial problems. Journal of Machine Learning Research, 7, 1409\u20131436.","journal-title":"Journal of Machine Learning Research"},{"key":"1320_CR2","doi-asserted-by":"crossref","unstructured":"B\u00fchler, T., & Hein, M. (2009). Spectral clustering based on the graph p-Laplacian. In Proceedings of the 26th international conference on machine learning (pp. 81\u201388).","DOI":"10.1145\/1553374.1553385"},{"key":"1320_CR3","doi-asserted-by":"crossref","unstructured":"Cai, X., Nie, F., Huang, H., & Kamangar, F. (2011). Heterogeneous image feature integration via multi-modal spectral clustering. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1977\u20131984). IEEE.","DOI":"10.1109\/CVPR.2011.5995740"},{"key":"1320_CR4","doi-asserted-by":"crossref","unstructured":"Chen, X., Hong, W., Nie, F., He, D., Yang, M., & Huang, J. Z. (2018). Spectral clustering of large-scale data by directly solving normalized cut. In Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1206\u20131215).","DOI":"10.1145\/3219819.3220039"},{"key":"1320_CR5","doi-asserted-by":"crossref","unstructured":"Chen, X., Huang, J. Z., Nie, F., Chen, R., & Wu, Q. (2017). A self-balanced min-cut algorithm for image clustering. In Proceedings of the international conference on computer vision (pp. 2080\u20132088).","DOI":"10.1109\/ICCV.2017.227"},{"key":"1320_CR6","doi-asserted-by":"crossref","unstructured":"Chen, X., Nie, F., Huang, J. Z., & Yang, M. (2017). Scalable normalized cut with improved spectral rotation. In Proceedings of the twenty-sixth international joint conference on artificial intelligence (pp. 1518\u20131524).","DOI":"10.24963\/ijcai.2017\/210"},{"issue":"4","key":"1320_CR7","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1109\/TKDE.2011.262","volume":"25","author":"X Chen","year":"2013","unstructured":"Chen, X., Xu, X., Ye, Y., & Huang, J. Z. (2013). TW-k-means: Automated two-level variable weighting clustering algorithm for multiview data. IEEE Transactions on Knowledge and Data Engineering, 25(4), 932\u2013944.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"1320_CR8","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1016\/j.patcog.2017.10.026","volume":"76","author":"X Chen","year":"2018","unstructured":"Chen, X., Yang, M., Huang, J. Z., & Zhong, M. (2018). TWCC: Automated two-way subspace weighting partitional co-clustering. Pattern Recognition, 76, 404\u2013415.","journal-title":"Pattern Recognition"},{"issue":"1","key":"1320_CR9","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1016\/j.patcog.2011.06.004","volume":"45","author":"X Chen","year":"2012","unstructured":"Chen, X., Ye, Y., Xu, X., & Huang, J. Z. (2012). A feature group weighting method for subspace clustering of high-dimensional data. Pattern Recognition, 45(1), 434\u2013446.","journal-title":"Pattern Recognition"},{"issue":"1","key":"1320_CR10","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1186\/1471-2105-9-497","volume":"9","author":"MC de Souto","year":"2008","unstructured":"de Souto, M. C., Costa, I. G., de Araujo, D. S., Ludermir, T. B., & Schliep, A. (2008). Clustering cancer gene expression data: A comparative study. BMC Bioinformatics, 9(1), 497.","journal-title":"BMC Bioinformatics"},{"issue":"11","key":"1320_CR11","doi-asserted-by":"publisher","first-page":"2765","DOI":"10.1109\/TPAMI.2013.57","volume":"35","author":"E Elhamifar","year":"2013","unstructured":"Elhamifar, E., & Vidal, R. (2013). Sparse subspace clustering: Algorithm, theory, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(11), 2765\u20132781.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"2","key":"1320_CR12","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1023\/A:1009745219419","volume":"2","author":"M Ester","year":"1998","unstructured":"Ester, M., Kriegel, H. P., & Xu, X. (1998). Density-based clustering in spatial databases: The algorithm GDBscan and its applications. Data Mining and Knowledge Discovery, 2(2), 169\u2013194.","journal-title":"Data Mining and Knowledge Discovery"},{"issue":"9","key":"1320_CR13","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1109\/43.159993","volume":"11","author":"L Hagen","year":"1992","unstructured":"Hagen, L., & Kahng, A. B. (1992). New spectral methods for ratio cut partitioning and clustering. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 11(9), 1074\u20131085.","journal-title":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"},{"key":"1320_CR14","doi-asserted-by":"crossref","unstructured":"Huang, J., Nie, F., & Hu, H. (2013). Spectral rotation versus k-means in spectral clustering. In AAAI conference on artificial intelligence (pp. 431\u2013437).","DOI":"10.1609\/aaai.v27i1.8683"},{"issue":"5","key":"1320_CR15","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/34.291440","volume":"16","author":"JJ Hull","year":"2002","unstructured":"Hull, J. J. (2002). Database for handwritten text recognition research. IEEE Transactions on Pattern Analysis & Machine Intelligence, 16(5), 550\u2013554.","journal-title":"IEEE Transactions on Pattern Analysis & Machine Intelligence"},{"issue":"3","key":"1320_CR16","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/BF02289588","volume":"32","author":"SC Johnson","year":"1967","unstructured":"Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241\u2013254.","journal-title":"Psychometrika"},{"issue":"6","key":"1320_CR17","doi-asserted-by":"publisher","first-page":"2833","DOI":"10.1109\/TIP.2016.2553459","volume":"25","author":"C Lu","year":"2016","unstructured":"Lu, C., Yan, S., & Lin, Z. (2016). Convex sparse spectral clustering: Single-view to multi-view. IEEE Transactions on Image Processing, 25(6), 2833\u20132843.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1320_CR18","first-page":"849","volume":"2","author":"AY Ng","year":"2002","unstructured":"Ng, A. Y., Jordan, M. I., Weiss, Y., et al. (2002). On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems, 2, 849\u2013856.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"1320_CR19","doi-asserted-by":"crossref","unstructured":"Nie, F., Wang, X., Jordan, M., & Huang, H. (2016). The constrained Laplacian rank algorithm for graph-based clustering. In Proceedings of the thirtieth AAAI conference on artificial intelligence (pp. 1969\u20131976).","DOI":"10.1609\/aaai.v30i1.10302"},{"key":"1320_CR20","doi-asserted-by":"crossref","unstructured":"Nie, F., Wang, X., & Huang, H. (2014). Clustering and projected clustering with adaptive neighbors. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 977\u2013986). ACM.","DOI":"10.1145\/2623330.2623726"},{"issue":"11","key":"1320_CR21","doi-asserted-by":"publisher","first-page":"1796","DOI":"10.1109\/TNN.2011.2162000","volume":"22","author":"F Nie","year":"2011","unstructured":"Nie, F., Zeng, Z., Tsang, I. W., Xu, D., & Zhang, C. (2011). Spectral embedded clustering: A framework for in-sample and out-of-sample spectral clustering. IEEE Transactions on Neural Networks, 22(11), 1796\u2013808.","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"3","key":"1320_CR22","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1609\/aimag.v29i3.2157","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., & Eliassi-Rad, T. (2008). Collective classification in network data. AI Magazine, 29(3), 93\u2013106.","journal-title":"AI Magazine"},{"issue":"8","key":"1320_CR23","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1109\/34.868688","volume":"22","author":"J Shi","year":"2000","unstructured":"Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888\u2013905.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"4","key":"1320_CR24","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U Von Luxburg","year":"2007","unstructured":"Von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17(4), 395\u2013416.","journal-title":"Statistics and Computing"},{"key":"1320_CR25","doi-asserted-by":"crossref","unstructured":"Yu, S. X., & Shi, J. (2003). Multiclass spectral clustering. In Proceedings of IEEE international conference on computer vision (vol. 1, pp. 313\u2013319).","DOI":"10.1109\/ICCV.2003.1238361"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-020-01320-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11263-020-01320-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-020-01320-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T04:03:39Z","timestamp":1695960219000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11263-020-01320-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,26]]},"references-count":25,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["1320"],"URL":"https:\/\/doi.org\/10.1007\/s11263-020-01320-3","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,26]]},"assertion":[{"value":"25 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}