{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T05:50:10Z","timestamp":1743141010358,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":10,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811989902"},{"type":"electronic","value":"9789811989919"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-8991-9_7","type":"book-chapter","created":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T08:04:02Z","timestamp":1674029042000},"page":"80-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Hot Spot Mining Technology for Network Public Opinion"],"prefix":"10.1007","author":[{"given":"Chengxin","family":"Xie","sequence":"first","affiliation":[]},{"given":"Yuxuan","family":"Han","sequence":"additional","affiliation":[]},{"given":"Yingxue","family":"Mu","sequence":"additional","affiliation":[]},{"given":"Xiumei","family":"Wen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"issue":"2","key":"7_CR1","doi-asserted-by":"publisher","first-page":"103487","DOI":"10.1016\/j.apergo.2021.103487","volume":"96","author":"S Kolose","year":"2021","unstructured":"Kolose, S., et al.: Cluster size prediction for military clothing using 3D body scan data. Appl. Ergon. 96(2), 103487\u2013103497 (2021)","journal-title":"Appl. Ergon."},{"key":"7_CR2","doi-asserted-by":"publisher","first-page":"165461","DOI":"10.1016\/j.nima.2021.165461","volume":"1010","author":"R Solli","year":"2021","unstructured":"Solli, R., et al.: Unsupervised learning for identifying events in active target experiments. Nucl. Instrum. Methods Phys. Res., Sect. A 1010, 165461 (2021)","journal-title":"Nucl. Instrum. Methods Phys. Res., Sect. A"},{"key":"7_CR3","doi-asserted-by":"publisher","first-page":"110228","DOI":"10.1016\/j.scienta.2021.110228","volume":"287","author":"AK Chandel","year":"2021","unstructured":"Chandel, A.K., et al.: Apple powdery mildew infestation detection and mapping using a high-resolution visible and multispectral aerial imaging technique. Scientia Horticulturae 287, 110228 (2021)","journal-title":"Scientia Horticulturae"},{"key":"7_CR4","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/s41060-021-00270-4","volume":"12","author":"A Kazuo","year":"2021","unstructured":"Kazuo, A.: CPI-model-based analysis of sparse K-means clustering algorithms. Int. J. Data Sci. Anal. 12, 229\u2013248 (2021)","journal-title":"Int. J. Data Sci. Anal."},{"issue":"9","key":"7_CR5","doi-asserted-by":"publisher","first-page":"2972","DOI":"10.3390\/s21092972","volume":"21","author":"A Marek","year":"2021","unstructured":"Marek, A.: Detection and classification of malicious flows in software-defined networks using data mining techniques. Sensors. 21(9), 2972 (2021)","journal-title":"Sensors."},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Daoud, A.S. et al.: Improving arabic document clustering using k-means algorithm and particle swarm optimization. In: Conference 2017, IntelliSys, pp.879\u2013885. IEEE Xplore (2017)","DOI":"10.1109\/IntelliSys.2017.8324233"},{"issue":"2","key":"7_CR7","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1049\/ntw2.12017","volume":"10","author":"AR Alharbi","year":"2021","unstructured":"Alharbi, A.R.: Enhancing topic clustering for Arabic security news based on K-means and topic modeling. IET Netw. 10(2), 278\u2013294 (2021)","journal-title":"IET Netw."},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, H., Liu, C., Zhang, M., Zhu, R.: A hot spot clustering method based on improved K-means algorithm. In: Conference 2017, (ICCWAMTIP), pp. 32\u201335. IEEE (2017)","DOI":"10.1109\/ICCWAMTIP.2017.8301443"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Zhang, W., Lu, J.: An online water army detection method based on network hot events. In: Conference 2018,ICMTMA, pp. 191\u2013193. IEEE Computer Society (2018)","DOI":"10.1109\/ICMTMA.2018.00053"},{"issue":"9","key":"7_CR10","doi-asserted-by":"publisher","first-page":"3871","DOI":"10.1007\/s00521-020-05442-0","volume":"33","author":"C Huang","year":"2020","unstructured":"Huang, C., Zhu, Z.: Complex communication application identification and private network mining technology under a large-scale network. Neural Comput. Appl. 33(9), 3871\u20133879 (2020). https:\/\/doi.org\/10.1007\/s00521-020-05442-0","journal-title":"Neural Comput. Appl."}],"container-title":["Communications in Computer and Information Science","Data Mining and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-8991-9_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T08:10:09Z","timestamp":1674029409000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-8991-9_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811989902","9789811989919"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-8991-9_7","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DMBD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Mining and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dmbd2022","order":10,"name":"conference_id","label":"Conference ID","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"135","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":"62","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":"46% - 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":"2.8","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":"2-3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}