{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:00:48Z","timestamp":1743058848353,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811607042"},{"type":"electronic","value":"9789811607059"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-16-0705-9_4","type":"book-chapter","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T13:03:53Z","timestamp":1617195833000},"page":"45-58","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Rotation-DPeak: Improving Density Peaks Selection for Imbalanced Data"],"prefix":"10.1007","author":[{"given":"Xiaoliang","family":"Hu","sequence":"first","affiliation":[]},{"given":"Ming","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Yewang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Lijie","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Jixiang","family":"Du","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,1]]},"reference":[{"issue":"2","key":"4_CR1","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/S0031-3203(02)00060-2","volume":"36","author":"A Likas","year":"2003","unstructured":"Likas, A., Vlassis, N., Verbeek, J.J.: The global k-means clustering algorithm. Pattern Recognit. 36(2), 451\u2013461 (2003)","journal-title":"Pattern Recognit."},{"issue":"16","key":"4_CR2","first-page":"3397","volume":"181","author":"C Zhong","year":"2011","unstructured":"Zhong, C., Miao, D., FrNti, P.: Minimum spanning tree based split-and-merge: a hierarchical clustering method. Inf. Ences 181(16), 3397\u20133410 (2011)","journal-title":"Inf. Ences"},{"key":"4_CR3","unstructured":"Wang, W., Yang, J., Muntz, R.: Sting: a statistical information grid approach to spatial data mining. In: Proceedings of 23rd International Conference Very Large Data Bases, VLDB 1997, Athens, Greece, pp. 186\u2013195 (1997)"},{"issue":"6191","key":"4_CR4","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1126\/science.1242072","volume":"344","author":"A Rodriguez","year":"2014","unstructured":"Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492\u20131496 (2014)","journal-title":"Science"},{"key":"4_CR5","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.patcog.2018.05.030","volume":"83","author":"Y Chen","year":"2018","unstructured":"Chen, Y., Tang, S., Bouguila, N., Wang, C., Du, J., Li, H.: A fast clustering algorithm based on pruning unnecessary distance computations in DBSCAN for high-dimensional data. Pattern Recognit. 83, 375\u2013387 (2018)","journal-title":"Pattern Recognit."},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: KNN-block DBSCAN: fast clustering for large-scale data. IEEE Trans. Syst. Man Cybern. Syst. 1\u201315 (2019)","DOI":"10.1109\/TSMC.2019.2956527"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhou, L., Bouguila, N., Wang, C., Chen, Y., Du, J.: Block-DBSCAN: fast clustering for large scale data. Pattern Recognit. 109, 107624 (2021)","DOI":"10.1016\/j.patcog.2020.107624"},{"key":"4_CR8","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1016\/j.knosys.2018.09.009","volume":"163","author":"Z Kang","year":"2019","unstructured":"Kang, Z., Wen, L., Chen, W., Xu, Z.: Low-rank kernel learning for graph-based clustering. Knowl. Based Syst. 163, 510\u2013517 (2019)","journal-title":"Knowl. Based Syst."},{"key":"4_CR9","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":"4_CR10","doi-asserted-by":"crossref","unstructured":"Xing, Y., Yu, G., Domeniconi, C., Wang, J., Zhang, Z., Guo, M.: Multi-view multi-instance multi-label learning based on collaborative matrix factorization, pp. 5508\u20135515 (2019)","DOI":"10.1609\/aaai.v33i01.33015508"},{"issue":"6","key":"4_CR11","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., 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":"4_CR12","doi-asserted-by":"crossref","unstructured":"Zhang, Z., et al.: Flexible auto-weighted local-coordinate concept factorization: a robust framework for unsupervised clustering. IEEE Trans. Knowl. Data Eng. 1 (2019)","DOI":"10.1109\/TKDE.2019.2940576"},{"issue":"1","key":"4_CR13","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s00521-016-2300-1","volume":"28","author":"Y Shi","year":"2016","unstructured":"Shi, Y., Chen, Z., Qi, Z., Meng, F., Cui, L.: A novel clustering-based image segmentation via density peaks algorithm with mid-level feature. Neural Comput. Appl. 28(1), 29\u201339 (2016). https:\/\/doi.org\/10.1007\/s00521-016-2300-1","journal-title":"Neural Comput. Appl."},{"issue":"22","key":"4_CR14","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.neucom.2016.11.019","volume":"226","author":"X Bai","year":"2017","unstructured":"Bai, X., Yang, P., Shi, X.: An overlapping community detection algorithm based on density peaks. Neurocomputing 226(22), 7\u201315 (2017)","journal-title":"Neurocomputing"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Liu, D., Su, Y., Li, X., Niu, Z.: A novel community detection method based on cluster density peaks. In: National CCF Conference on Natural Language Processing & Chinese Computing, vol. PP, pp. 515\u2013525 (2017)","DOI":"10.1007\/978-3-319-73618-1_43"},{"issue":"1","key":"4_CR16","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.trit.2016.12.005","volume":"2","author":"B Wang","year":"2017","unstructured":"Wang, B., Zhang, J., Liu, Y.: Density peaks clustering based integrate framework for multi-document summarization. CAAI Trans. Intell. Technol. 2(1), 26\u201330 (2017)","journal-title":"CAAI Trans. Intell. Technol."},{"issue":"3","key":"4_CR17","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1049\/cje.2016.05.001","volume":"25","author":"C Li","year":"2016","unstructured":"Li, C., Ding, G., Wang, D., Yan, L., Wang, S.: Clustering by fast search and find of density peaks with data field. Chin. J. Electron. 25(3), 397\u2013402 (2016)","journal-title":"Chin. J. Electron."},{"issue":"3","key":"4_CR18","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1007\/s00779-018-1112-y","volume":"22","author":"R Mehmood","year":"2018","unstructured":"Mehmood, R., El-Ashram, S., Bie, R., Sun, Y.: Effective cancer subtyping by employing density peaks clustering by using gene expression microarray. Pers. Ubiquit. Comput. 22(3), 615\u2013619 (2018). https:\/\/doi.org\/10.1007\/s00779-018-1112-y","journal-title":"Pers. Ubiquit. Comput."},{"key":"4_CR19","doi-asserted-by":"publisher","unstructured":"Cheng, D., Zhu, Q., Huang, J., Wu, Q., Lijun, Y.: Clustering with local density peaks-based minimum spanning tree. IEEE Trans. Knowl. Data Eng. PP(99), 1 (2019). https:\/\/doi.org\/10.1109\/TKDE.2019.2930056","DOI":"10.1109\/TKDE.2019.2930056"},{"key":"4_CR20","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Fast density peak clustering for large scale data based on KNN. Knowl. Based Syst. 187, 104824 (2020)","DOI":"10.1016\/j.knosys.2019.06.032"},{"key":"4_CR21","first-page":"649","volume":"433\u2013434","author":"Y Chen","year":"2018","unstructured":"Chen, Y., et al.: Decentralized clustering by finding loose and distributed density cores. Inf. Sci. 433\u2013434, 649\u2013660 (2018)","journal-title":"Inf. Sci."},{"key":"4_CR22","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.knosys.2017.07.010","volume":"133","author":"L Yaohui","year":"2017","unstructured":"Yaohui, L., Zhengming, M., Fang, Y.: Adaptive density peak clustering based on k-nearest neighbors with aggregating strategy. Knowl. Based Syst. 133, 208\u2013220 (2017)","journal-title":"Knowl. Based Syst."},{"key":"4_CR23","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.patrec.2016.01.009","volume":"73","author":"Z Liang","year":"2016","unstructured":"Liang, Z., Chen, P.: Delta-density based clustering with a divide-and-conquer strategy: 3DC clustering. Pattern Recognit. Lett. 73, 52\u201359 (2016)","journal-title":"Pattern Recognit. Lett."},{"issue":"6","key":"4_CR24","doi-asserted-by":"publisher","first-page":"2800","DOI":"10.1177\/0962280215609948","volume":"26","author":"XF Wang","year":"2017","unstructured":"Wang, X.F., Xu, Y.: Fast clustering using adaptive density peak detection. Stat. Methods Med. Res. 26(6), 2800\u20132811 (2017)","journal-title":"Stat. Methods Med. Res."},{"key":"4_CR25","unstructured":"Ding, J., He, X., Yuan, J., Jiang, B.: Automatic clustering based on density peak detection using generalized extreme value distribution. In: Soft Computing. A Fusion of Foundations Methodologies & Applications, pp. 515\u2013525 (2018)"}],"container-title":["Communications in Computer and Information Science","Big Data"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-0705-9_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T13:05:52Z","timestamp":1617195952000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-16-0705-9_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811607042","9789811607059"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-0705-9_4","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 April 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BigData","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF Conference on Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chongqing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bigdat2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/bigdata2020.swu.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CCF online submission system","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"65","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":"16","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":"0","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":"25% - 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":"3","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)"}}]}}