{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T06:53:07Z","timestamp":1726123987363},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811625398"},{"type":"electronic","value":"9789811625404"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/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":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-16-2540-4_47","type":"book-chapter","created":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T18:04:30Z","timestamp":1620324270000},"page":"639-649","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Design and Implementation of KMeans Based on Unified Batch and Streaming Processing"],"prefix":"10.1007","author":[{"given":"Hao","family":"Chen","sequence":"first","affiliation":[]},{"given":"Kun","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Yuzhong","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Hong","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,7]]},"reference":[{"key":"47_CR1","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1016\/j.neucom.2017.06.053","volume":"267","author":"A Saxena","year":"2017","unstructured":"Saxena, A., Prasad, M., Gupta, A., et al.: A review of clustering techniques and developments. Neurocomputing 267, 664\u2013681 (2017)","journal-title":"Neurocomputing"},{"issue":"3","key":"47_CR2","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/s10115-014-0808-1","volume":"45","author":"H-L Nguyen","year":"2014","unstructured":"Nguyen, H.-L., Woon, Y.-K., Ng, W.-K.: A survey on data stream clustering and classification. Knowl. Inf. Syst. 45(3), 535\u2013569 (2014). https:\/\/doi.org\/10.1007\/s10115-014-0808-1","journal-title":"Knowl. Inf. Syst."},{"key":"47_CR3","doi-asserted-by":"crossref","unstructured":"Young, S., Arel, I., Karnowski, T.P., Rose, D.: A fast and stable incremental clustering algorithm. In: 2010 Seventh International Conference on Information Technology: New Generations, pp. 204\u2013209 (2010)","DOI":"10.1109\/ITNG.2010.148"},{"key":"47_CR4","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C., Philip, S.Y., Han, J., Wang, J.: A framework for clustering evolving data streams. In: Proceedings 2003 VLDB Conference, pp. 81\u201392 (2003)","DOI":"10.1016\/B978-012722442-8\/50016-1"},{"key":"47_CR5","doi-asserted-by":"crossref","unstructured":"Cao, F., Estert, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: Proceedings of the 2006 SIAM International Conference on Data Mining, pp. 328\u2013339 (2006)","DOI":"10.1137\/1.9781611972764.29"},{"key":"47_CR6","unstructured":"Apache Hadoop. https:\/\/hadoop.apache.org\/. Accessed 04 June 2020"},{"key":"47_CR7","unstructured":"Apache Spark. https:\/\/Spark.apache.org\/. Accessed 05 June 2020"},{"key":"47_CR8","unstructured":"Apache Flink. https:\/\/Flink.apache.org\/. Accessed 09 June 2020"},{"key":"47_CR9","doi-asserted-by":"crossref","unstructured":"Chintapalli, S., Dagit, D., Evans, B., et al.: Benchmarking streaming computation engines: storm, Flink and spark streaming. In: 2016 IEEE International Parallel and Distributed Processing Symposium Workshops, pp. 1789\u20131792 (2016)","DOI":"10.1109\/IPDPSW.2016.138"},{"key":"47_CR10","unstructured":"MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, no. 14, pp. 281\u2013297 (1967)"},{"issue":"4","key":"47_CR11","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1016\/j.patcog.2010.10.018","volume":"44","author":"AM Bagirov","year":"2011","unstructured":"Bagirov, A.M., Ugon, J., Webb, D.: Fast modified global k-means algorithm for incremental cluster construction. Pattern Recogn. 44(4), 866\u2013876 (2011)","journal-title":"Pattern Recogn."},{"issue":"7","key":"47_CR12","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1243\/0954406041319509","volume":"218","author":"DT Pham","year":"2004","unstructured":"Pham, D.T., Dimov, S.S., Nguyen, C.D.: An incremental K-means algorithm. Proc. Inst. Mech. Eng. J. Mech. Eng. Sci. 218(7), 783\u2013795 (2004)","journal-title":"Proc. Inst. Mech. Eng. J. Mech. Eng. Sci."},{"key":"47_CR13","doi-asserted-by":"crossref","unstructured":"Cao, F., Estert, M., Qian, W., et al.: Density-based clustering over an evolving data stream with noise. In: Proceedings of the 2006 SIAM International Conference on Data Mining, pp. 328\u2013339 (2006)","DOI":"10.1137\/1.9781611972764.29"},{"key":"47_CR14","unstructured":"Apache Mahout. https:\/\/mahout.apache.org. Accessed 05 June 2020"},{"issue":"1","key":"47_CR15","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1145\/1327452.1327492","volume":"51","author":"J Dean","year":"2008","unstructured":"Dean, J., Ghemawat, S.: MapReduce: Simplified data processing on large clusters. Commun. ACM 51(1), 107\u2013113 (2008)","journal-title":"Commun. ACM"},{"key":"47_CR16","doi-asserted-by":"crossref","unstructured":"McCallum, A., Nigam, K., Ungar, L.H.: Efficient clustering of high-dimensional data sets with application to reference matching. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 169\u2013178 (2000)","DOI":"10.1145\/347090.347123"},{"key":"47_CR17","doi-asserted-by":"publisher","unstructured":"Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Springer, Boston (2013). https:\/\/doi.org\/10.1007\/978-1-4757-0450-1","DOI":"10.1007\/978-1-4757-0450-1"},{"key":"47_CR18","doi-asserted-by":"crossref","unstructured":"Wei, X., Croft, W.B.: LDA-based document models for ad-hoc retrieval. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 178\u2013185 (2006)","DOI":"10.1145\/1148170.1148204"},{"key":"47_CR19","unstructured":"Spark StreamingKMeans. https:\/\/Spark.apache.org\/docs\/latest\/ml-guide.html. Accessed 08 June 2020"},{"key":"47_CR20","unstructured":"Alibaba Alink. https:\/\/github.com\/alibaba\/Alink. Accessed 08 June 2020"},{"key":"47_CR21","doi-asserted-by":"crossref","unstructured":"Marcu, O.C., Costan, A., Antoniu, G., P\u00e9rez-Hern\u00e1ndez, M.S.: Spark versus Flink: understanding performance in big data analytics frameworks. In: 2016 IEEE International Conference on Cluster Computing, pp. 433\u2013442 (2016)","DOI":"10.1109\/CLUSTER.2016.22"},{"key":"47_CR22","unstructured":"UCI Machine Learning. https:\/\/archive.ics.uci.edu\/ml\/index.php. Accessed 07 May 2020"},{"key":"47_CR23","volume-title":"Machine Learning","author":"Z-H Zhou","year":"2016","unstructured":"Zhou, Z.-H.: Machine Learning, 2nd edn. Tsinghua University Press, Beijing (2016)","edition":"2"}],"container-title":["Communications in Computer and Information Science","Computer Supported Cooperative Work and Social Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-2540-4_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T18:14:03Z","timestamp":1620324843000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-2540-4_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811625398","9789811625404"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-2540-4_47","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":"7 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ChineseCSCW","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF Conference on Computer Supported Cooperative Work and Social Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","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":"7 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 November 2020","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":"chinesecscw2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.scholat.com\/confweb\/CCSCW2020","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}