{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T16:14:16Z","timestamp":1747152856806,"version":"3.40.5"},"publisher-location":"Cham","reference-count":11,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030352301"},{"type":"electronic","value":"9783030352318"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-35231-8_64","type":"book-chapter","created":{"date-parts":[[2019,11,15]],"date-time":"2019-11-15T19:30:38Z","timestamp":1573846238000},"page":"863-870","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Application of Weighted K-Means Decision Cluster Classifier in the Recognition of Infectious Expressions of Primary School Students Reading"],"prefix":"10.1007","author":[{"given":"Dongqing","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9705-8377","authenticated-orcid":false,"given":"Zhenyu","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,15]]},"reference":[{"key":"64_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74048-3","volume-title":"Information Retrieval for Music and Motion, 65","author":"M M\u00fcller","year":"2007","unstructured":"M\u00fcller, M.: Information Retrieval for Music and Motion, 65. Springer, Heidelberg (2007). \nhttps:\/\/doi.org\/10.1007\/978-3-540-74048-3"},{"key":"64_CR2","unstructured":"M\u00fcller, A.C., Guido, S.: Introduction to Machine Learning with Python. O\u2019Reilly Media (2016)"},{"key":"64_CR3","doi-asserted-by":"crossref","unstructured":"Wang, Y., Song, C., Xia, S.T.: Improving decision trees by Tsallis Entropy Information Metric method. In: International Joint Conference on Neural Networks (2016)","DOI":"10.1109\/IJCNN.2016.7727821"},{"key":"64_CR4","doi-asserted-by":"crossref","unstructured":"Guan, X., Liang, J., Qian, Y., et al.: A multi-view OVA model based on decision tree for multi-classification tasks. Knowledge-Based Systems (2017). S0950705117304641","DOI":"10.1016\/j.knosys.2017.10.004"},{"key":"64_CR5","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.knosys.2015.02.019","volume":"82","author":"A.E. Gutierrez-Rodr\u00edguez","year":"2015","unstructured":"Gutierrez-Rodr\u00edguez, A.E., Mart\u00ednez-Trinidad, J.F., Garc\u00eda-Borroto, M., et al.: Mining patterns for clustering on numerical datasets using unsupervised decision trees[J]. Knowledge-Based Systems, 2015, 82:70\u201379","journal-title":"Knowledge-Based Systems"},{"issue":"1","key":"64_CR6","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s13042-015-0328-7","volume":"8","author":"J Tanha","year":"2017","unstructured":"Tanha, J., Someren, M.V., Afsarmanesh, H.: Semi-supervised self-training for decision tree classifiers. Int. J. Mach. Learn. Cybern. 8(1), 355\u2013370 (2017)","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"64_CR7","doi-asserted-by":"crossref","unstructured":"Katuwal, R., Suganthan, P.N.: Enhancing multi-class classification of random forest using random vector functional neural network and oblique decision surfaces (2018)","DOI":"10.1109\/IJCNN.2018.8489738"},{"key":"64_CR8","first-page":"81","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81\u2013106 (1986)","journal-title":"Mach. Learn."},{"key":"64_CR9","unstructured":"Quinlan, J.R.: C4.5: programs for machine learning (1992)"},{"issue":"2","key":"64_CR10","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/BF01889584","volume":"2","author":"W Buntine","year":"1992","unstructured":"Buntine, W.: Learning classification trees. Stat. Comput. 2(2), 63\u201373 (1992)","journal-title":"Stat. Comput."},{"key":"64_CR11","unstructured":"Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 1\u201322 (1998)"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-35231-8_64","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,15]],"date-time":"2019-11-15T19:45:19Z","timestamp":1573847119000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-35231-8_64"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030352301","9783030352318"],"references-count":11,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-35231-8_64","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"15 November 2019","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":"Dalian","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/adma2019.neusoft.edu.cn\/","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"170","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":"39","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":"26","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":"23% - 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":"7","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}