{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T15:32:54Z","timestamp":1743089574945,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030954079"},{"type":"electronic","value":"9783030954086"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-95408-6_27","type":"book-chapter","created":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T00:04:07Z","timestamp":1643587447000},"page":"356-367","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Balanced Spectral Clustering Algorithm Based on Feature Selection"],"prefix":"10.1007","author":[{"given":"Qimin","family":"Luo","sequence":"first","affiliation":[]},{"given":"Guangquan","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Guoqiu","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Zidong","family":"Su","sequence":"additional","affiliation":[]},{"given":"Xingyi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Wei","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,31]]},"reference":[{"issue":"7","key":"27_CR1","doi-asserted-by":"publisher","first-page":"1982","DOI":"10.1007\/s11263-020-01320-3","volume":"128","author":"X Chen","year":"2020","unstructured":"Chen, X., Hong, W., Nie, F., Huang, J.Z., Shen, L.: Enhanced balanced min cut. Int. J. Comput. Vis. 128(7), 1982\u20131995 (2020)","journal-title":"Int. J. Comput. Vis."},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhexue Haung, J., Nie, F., Chen, R., Wu, Q.: A self-balanced min-cut algorithm for image clustering. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2061\u20132069 (2017)","DOI":"10.1109\/ICCV.2017.227"},{"issue":"11","key":"27_CR3","doi-asserted-by":"publisher","first-page":"6611","DOI":"10.1007\/s00521-018-3852-z","volume":"32","author":"T Du","year":"2018","unstructured":"Du, T., Wen, G., Cai, Z., Zheng, W., Tan, M., Li, Y.: Spectral clustering algorithm combining local covariance matrix with normalization. Neural Comput. Appl. 32(11), 6611\u20136618 (2018). https:\/\/doi.org\/10.1007\/s00521-018-3852-z","journal-title":"Neural Comput. Appl."},{"key":"27_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102057","volume":"71","author":"J Gan","year":"2021","unstructured":"Gan, J., Peng, Z., Zhu, X., Hu, R., Ma, J., Wu, G.: Brain functional connectivity analysis based on multi-graph fusion. Med. Image Anal. 71, 102057 (2021)","journal-title":"Med. Image Anal."},{"key":"27_CR5","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.neucom.2019.01.108","volume":"391","author":"Y Guo","year":"2020","unstructured":"Guo, Y., Wu, Z., Shen, D.: Learning longitudinal classification-regression model for infant hippocampus segmentation. Neurocomputing 391, 191\u2013198 (2020)","journal-title":"Neurocomputing"},{"issue":"1","key":"27_CR6","first-page":"100","volume":"28","author":"JA Hartigan","year":"1979","unstructured":"Hartigan, J.A., Wong, M.A.: Algorithm as 136:a k-means clustering algorithm. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100\u2013108 (1979)","journal-title":"J. Roy. Stat. Soc. Ser. C (Appl. Stat.)"},{"key":"27_CR7","doi-asserted-by":"publisher","first-page":"3843","DOI":"10.1109\/TMI.2021.3099641","volume":"40","author":"R Hu","year":"2021","unstructured":"Hu, R., et al.: Multi-band brain network analysis for functional neuroimaging biomarker identification. IEEE Trans. Med. Imaging 40, 3843\u20133855 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"27_CR8","doi-asserted-by":"publisher","first-page":"1945","DOI":"10.1007\/s11280-019-00766-x","volume":"23","author":"R Hu","year":"2020","unstructured":"Hu, R., Zhu, X., Zhu, Y., Gan, J.: Robust SVM with adaptive graph learning. World Wide Web 23(3), 1945\u20131968 (2020)","journal-title":"World Wide Web"},{"issue":"6","key":"27_CR9","doi-asserted-by":"publisher","first-page":"1212","DOI":"10.1109\/TKDE.2019.2903410","volume":"32","author":"D Huang","year":"2019","unstructured":"Huang, D., Wang, C.D., Wu, J.S., Lai, J.H., Kwoh, C.K.: Ultra-scalable spectral clustering and ensemble clustering. IEEE Trans. Knowl. Data Eng. 32(6), 1212\u20131226 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"27_CR10","volume-title":"Algorithms for Clustering Data","author":"AK Jain","year":"1988","unstructured":"Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall Inc., Upper Saddle River (1988)"},{"key":"27_CR11","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.neunet.2019.10.010","volume":"122","author":"Z Kang","year":"2020","unstructured":"Kang, Z., et al.: Partition level multiview subspace clustering. Neural Netw. 122, 279\u2013288 (2020)","journal-title":"Neural Netw."},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Li, Z., Nie, F., Chang, X., Ma, Z., Yang, Y.: Balanced clustering via exclusive lasso: A pragmatic approach. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.11702"},{"key":"27_CR13","doi-asserted-by":"crossref","unstructured":"Liu, H., Han, J., Nie, F., Li, X.: Balanced clustering with least square regression. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.10877"},{"key":"27_CR14","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/978-3-319-07176-3_65","volume-title":"Artificial Intelligence and Soft Computing","author":"M Luci\u0144ska","year":"2014","unstructured":"Luci\u0144ska, M.: A spectral clustering algorithm based on eigenvector localization. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS (LNAI), vol. 8468, pp. 749\u2013759. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-07176-3_65"},{"issue":"3","key":"27_CR15","doi-asserted-by":"publisher","first-page":"1913","DOI":"10.1007\/s11063-020-10297-6","volume":"52","author":"Q Luo","year":"2020","unstructured":"Luo, Q., Wen, G., Zhang, L., Zhan, M.: An efficient algorithm combining spectral clustering with feature selection. Neural Process. Lett. 52(3), 1913\u20131925 (2020)","journal-title":"Neural Process. Lett."},{"key":"27_CR16","unstructured":"Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems, pp. 849\u2013856 (2002)"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Nie, F., Wang, C.L., Li, X.: K-multiple-means: a multiple-means clustering method with specified k clusters. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 959\u2013967 (2019)","DOI":"10.1145\/3292500.3330846"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Nie, F., Wang, X., Jordan, M.I., Huang, H.: The constrained Laplacian rank algorithm for graph-based clustering. In: AAAI, pp. 1969\u20131976. Citeseer (2016)","DOI":"10.1609\/aaai.v30i1.10302"},{"issue":"11","key":"27_CR19","doi-asserted-by":"publisher","first-page":"112101","DOI":"10.1007\/s11432-016-9021-9","volume":"60","author":"F Nie","year":"2017","unstructured":"Nie, F., Zhang, R., Li, X.: A generalized power iteration method for solving quadratic problem on the Stiefel manifold. Sci. China Inf. Sci. 60(11), 112101 (2017)","journal-title":"Sci. China Inf. Sci."},{"key":"27_CR20","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.inffus.2020.08.023","volume":"66","author":"HT Shen","year":"2021","unstructured":"Shen, H.T., et al.: Heterogeneous data fusion for predicting mild cognitive impairment conversion. Inf. Fusion 66, 54\u201363 (2021)","journal-title":"Inf. Fusion"},{"key":"27_CR21","doi-asserted-by":"publisher","first-page":"3122","DOI":"10.1109\/TNNLS.2020.3009632","volume":"32","author":"HT Shen","year":"2020","unstructured":"Shen, H.T., Zhu, Y., Zheng, W., Zhu, X.: Half-quadratic minimization for unsupervised feature selection on incomplete data. IEEE Trans. Neural Netw. Learn. Syst. 32, 3122\u20133135 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"4","key":"27_CR22","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.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395\u2013416 (2007)","journal-title":"Stat. Comput."},{"issue":"10","key":"27_CR23","doi-asserted-by":"publisher","first-page":"4290","DOI":"10.1109\/TNNLS.2019.2953675","volume":"31","author":"G Xie","year":"2020","unstructured":"Xie, G., et al.: SRSC: selective, robust, and supervised constrained feature representation for image classification. IEEE Trans. Neural Networks Learn. Syst. 31(10), 4290\u20134302 (2020)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"4","key":"27_CR24","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1109\/TKDE.2018.2842190","volume":"31","author":"Y Yang","year":"2018","unstructured":"Yang, Y., Duan, Y., Wang, X., Huang, Z., Xie, N., Shen, H.T.: Hierarchical multi-clue modelling for poi popularity prediction with heterogeneous tourist information. IEEE Trans. Knowl. Data Eng. 31(4), 757\u2013768 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"6","key":"27_CR25","doi-asserted-by":"publisher","first-page":"102733","DOI":"10.1016\/j.ipm.2021.102733","volume":"58","author":"C Yuan","year":"2021","unstructured":"Yuan, C., Zhong, Z., Lei, C., Zhu, X., Hu, R.: Adaptive reverse graph learning for robust subspace learning. Inf. Process. Manage. 58(6), 102733 (2021)","journal-title":"Inf. Process. Manage."},{"issue":"3","key":"27_CR26","first-page":"1","volume":"8","author":"S Zhang","year":"2017","unstructured":"Zhang, S., Li, X., Zong, M., Zhu, X., Cheng, D.: Learning K for KNN classification. ACM Trans. Intell. Syst. Technol. (TIST) 8(3), 1\u201319 (2017)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"issue":"2","key":"27_CR27","doi-asserted-by":"publisher","first-page":"26018","DOI":"10.1088\/1741-2560\/9\/2\/026018","volume":"9","author":"Y Zhang","year":"2012","unstructured":"Zhang, Y., Zhao, Q., Jin, J., Wang, X., Cichocki, A.: A novel BCI based on ERP components sensitive to configural processing of human faces. J. Neural Eng. 9(2), 26018 (2012)","journal-title":"J. Neural Eng."},{"issue":"7","key":"27_CR28","doi-asserted-by":"publisher","first-page":"1774","DOI":"10.1109\/TPAMI.2018.2847335","volume":"41","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., Liu, L., Shen, F., Shen, H.T., Shao, L.: Binary multi-view clustering. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1774\u20131782 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"27_CR29","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1109\/JSTSP.2019.2957952","volume":"14","author":"Y Zhou","year":"2019","unstructured":"Zhou, Y., Tian, L., Zhu, C., Jin, X., Sun, Y.: Video coding optimization for virtual reality 360-degree source. IEEE J. Sel. Top. Sig. Process. 14(1), 118\u2013129 (2019)","journal-title":"IEEE J. Sel. Top. Sig. Process."},{"issue":"3","key":"27_CR30","doi-asserted-by":"publisher","first-page":"1969","DOI":"10.1007\/s11280-019-00731-8","volume":"23","author":"X Zhu","year":"2019","unstructured":"Zhu, X., Gan, J., Lu, G., Li, J., Zhang, S.: Spectral clustering via half-quadratic optimization. World Wide Web 23(3), 1969\u20131988 (2019). https:\/\/doi.org\/10.1007\/s11280-019-00731-8","journal-title":"World Wide Web"},{"issue":"2","key":"27_CR31","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1109\/TCYB.2015.2403356","volume":"46","author":"X Zhu","year":"2016","unstructured":"Zhu, X., Li, X., Zhang, S.: Block-row sparse multiview multilabel learning for image classification. IEEE Trans. Cybern. 46(2), 450\u2013461 (2016)","journal-title":"IEEE Trans. Cybern."},{"issue":"6","key":"27_CR32","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TNNLS.2016.2521602","volume":"28","author":"X Zhu","year":"2017","unstructured":"Zhu, X., Li, X., Zhang, S., Ju, C., Wu, X.: Robust joint graph sparse coding for unsupervised spectral feature selection. IEEE Trans. Neural Netw. Learn. Syst. 28(6), 1263\u20131275 (2017)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"9","key":"27_CR33","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1109\/TMM.2017.2703636","volume":"19","author":"X Zhu","year":"2017","unstructured":"Zhu, X., Li, X., Zhang, S., Xu, Z., Yu, L., Wang, C.: Graph PCA hashing for similarity search. IEEE Trans. Multimedia 19(9), 2033\u20132044 (2017)","journal-title":"IEEE Trans. Multimedia"},{"key":"27_CR34","doi-asserted-by":"publisher","first-page":"101824","DOI":"10.1016\/j.media.2020.101824","volume":"67","author":"X Zhu","year":"2021","unstructured":"Zhu, X., et al.: Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan. Med. Image Anal. 67, 101824 (2021)","journal-title":"Med. Image Anal."},{"key":"27_CR35","doi-asserted-by":"publisher","first-page":"2425","DOI":"10.1109\/TKDE.2019.2956530","volume":"33","author":"X Zhu","year":"2019","unstructured":"Zhu, X., Yang, J., Zhang, C., Zhang, S.: Efficient utilization of missing data in cost-sensitive learning. IEEE Trans. Knowl. Data Eng. 33, 2425\u20132436 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"8","key":"27_CR36","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TKDE.2018.2858782","volume":"31","author":"X Zhu","year":"2018","unstructured":"Zhu, X., Zhang, S., Li, Y., Zhang, J., Yang, L., Fang, Y.: Low-rank sparse subspace for spectral clustering. IEEE Trans. Knowl. Data Eng. 31(8), 1532\u20131543 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"27_CR37","doi-asserted-by":"crossref","unstructured":"Zhu, X., Zhang, S., Zhu, Y., Zhu, P., Gao, Y.: Unsupervised spectral feature selection with dynamic hyper-graph learning. IEEE Trans. Knowl. Data Eng., 1 (2020)","DOI":"10.1109\/TKDE.2020.3017250"},{"key":"27_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107175","volume":"105","author":"X Zhu","year":"2020","unstructured":"Zhu, X., Zhu, Y., Zheng, W.: Spectral rotation for deep one-step clustering. Pattern Recogn. 105, 107175 (2020)","journal-title":"Pattern Recogn."},{"key":"27_CR39","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.inffus.2021.07.013","volume":"77","author":"Y Zhu","year":"2022","unstructured":"Zhu, Y., Ma, J., Yuan, C., Zhu, X.: Interpretable learning based dynamic graph convolutional networks for Alzheimer\u2019s disease analysis. Inf. Fusion 77, 53\u201361 (2022)","journal-title":"Inf. Fusion"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-95408-6_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T06:43:22Z","timestamp":1674629002000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-95408-6_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030954079","9783030954086"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-95408-6_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"31 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 February 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 February 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/adma2021.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT3","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"116","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}