{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T04:12:39Z","timestamp":1746504759806},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319242811"},{"type":"electronic","value":"9783319242828"}],"license":[{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015]]},"DOI":"10.1007\/978-3-319-24282-8_24","type":"book-chapter","created":{"date-parts":[[2015,10,3]],"date-time":"2015-10-03T21:18:36Z","timestamp":1443907116000},"page":"284-298","source":"Crossref","is-referenced-by-count":1,"title":["Benchmarking Stream Clustering for Churn Detection in Dynamic Networks"],"prefix":"10.1007","author":[{"given":"Serdar Baran","family":"Tatar","sequence":"first","affiliation":[]},{"given":"Andrew","family":"McIntyre","sequence":"additional","affiliation":[]},{"given":"Nur","family":"Zincir-Heywood","sequence":"additional","affiliation":[]},{"given":"Malcolm","family":"Heywood","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2015,11,25]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases, vol. 29, pp. 81\u201392. VLDB Endowment (2003)","key":"24_CR1","DOI":"10.1016\/B978-012722442-8\/50016-1"},{"doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for projected clustering of high dimensional data streams. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, vol. 30, pp. 852\u2013863. VLDB Endowment (2004)","key":"24_CR2","DOI":"10.1016\/B978-012088469-8\/50075-9"},{"issue":"17","key":"24_CR3","doi-asserted-by":"publisher","first-page":"7889","DOI":"10.1016\/j.eswa.2014.06.018","volume":"41","author":"\u00d6G Ali","year":"2014","unstructured":"Ali, \u00d6.G., Ar\u0131t\u00fcrk, U.: Dynamic churn prediction framework with more effective use of rare event data: The case of private banking. Expert Syst. Appl. 41(17), 7889\u20137903 (2014)","journal-title":"Expert Syst. Appl."},{"unstructured":"Bifet, A., Holmes, G., Pfahringer, B., Kranen, P., Kremer, H., Jansen, T., Seidl, T.: Moa: Massive online analysis, a framework for stream classification and clustering (2010)","key":"24_CR4"},{"doi-asserted-by":"crossref","unstructured":"Cao, F., Ester, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: SDM, vol. 6, pp. 328\u2013339. SIAM (2006)","key":"24_CR5","DOI":"10.1137\/1.9781611972764.29"},{"key":"24_CR6","volume-title":"Swarm Intelligence","author":"RC Eberhart","year":"2001","unstructured":"Eberhart, R.C., Shi, Y., Kennedy, J.: Swarm Intelligence. Elsevier, London (2001)"},{"key":"24_CR7","first-page":"226","volume":"96","author":"M Ester","year":"1996","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd. 96, 226\u2013231 (1996)","journal-title":"Kdd."},{"doi-asserted-by":"crossref","unstructured":"Forestiero, A., Pizzuti, C., Spezzano, G.: Flockstream: a bio-inspired algorithm for clustering evolving data streams. In: 21st International Conference on Tools with Artificial Intelligence, ICTAI 2009, pp. 1\u20138. IEEE (2009)","key":"24_CR8","DOI":"10.1109\/ICTAI.2009.60"},{"doi-asserted-by":"crossref","unstructured":"Guttman, A.: R-trees: a dynamic index structure for spatial searching, vol. 14. ACM (1984)","key":"24_CR9","DOI":"10.1145\/971697.602266"},{"doi-asserted-by":"crossref","unstructured":"Guyon, I., Lemaire, V., Boull\u00e9, M., Dror, G., Vogel, D.: Analysis of the kdd cup 2009: Fast scoring on a large orange customer database (2009)","key":"24_CR10","DOI":"10.1145\/1809400.1809414"},{"issue":"5","key":"24_CR11","doi-asserted-by":"publisher","first-page":"3657","DOI":"10.1016\/j.eswa.2009.10.025","volume":"37","author":"BQ Huang","year":"2010","unstructured":"Huang, B.Q., Kechadi, T.M., Buckley, B., Kiernan, G., Keogh, E., Rashid, T.: A new feature set with new window techniques for customer churn prediction in land-line telecommunications. Expert Syst. Appl. 37(5), 3657\u20133665 (2010)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"24_CR12","doi-asserted-by":"publisher","first-page":"1814","DOI":"10.1016\/j.eswa.2010.07.110","volume":"38","author":"A Karahoca","year":"2011","unstructured":"Karahoca, A., Karahoca, D.: Gsm churn management by using fuzzy c-means clustering and adaptive neuro fuzzy inference system. Expert Syst. Appl. 38(3), 1814\u20131822 (2011)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"24_CR13","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s10115-010-0342-8","volume":"29","author":"P Kranen","year":"2011","unstructured":"Kranen, P., Assent, I., Baldauf, C., Seidl, T.: The clustree: indexing micro-clusters for anytime stream mining. Knowl. Inf. Syst. 29(2), 249\u2013272 (2011)","journal-title":"Knowl. Inf. Syst."},{"issue":"1","key":"24_CR14","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.dss.2011.12.014","volume":"53","author":"YH Lee","year":"2012","unstructured":"Lee, Y.H., Wei, C.P., Cheng, T.H., Yang, C.T.: Nearest-neighbor-based approach to time-series classification. Decis. Support Syst. 53(1), 207\u2013217 (2012)","journal-title":"Decis. Support Syst."},{"unstructured":"Moise, G., Sander, J., Ester, M.: P3c: A robust projected clustering algorithm. In: Sixth International Conference on Data Mining, 2006, ICDM 2006, pp. 414\u2013425. IEEE (2006)","key":"24_CR15"},{"issue":"3","key":"24_CR16","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1109\/72.846740","volume":"11","author":"MC Mozer","year":"2000","unstructured":"Mozer, M.C., Wolniewicz, R., Grimes, D.B., Johnson, E., Kaushansky, H.: Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. IEEE Trans. Neural Netw. 11(3), 690\u2013696 (2000)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"2","key":"24_CR17","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1509\/jmkr.43.2.204","volume":"43","author":"SA Neslin","year":"2006","unstructured":"Neslin, S.A., Gupta, S., Kamakura, W., Lu, J., Mason, C.H.: Defection detection: Measuring and understanding the predictive accuracy of customer churn models. J. Mark. Res. 43(2), 204\u2013211 (2006)","journal-title":"J. Mark. Res."},{"issue":"3","key":"24_CR18","doi-asserted-by":"publisher","first-page":"6714","DOI":"10.1016\/j.eswa.2008.08.050","volume":"36","author":"PC Pendharkar","year":"2009","unstructured":"Pendharkar, P.C.: Genetic algorithm based neural network approaches for predicting churn in cellular wireless network services. Expert Syst. Appl. 36(3), 6714\u20136720 (2009)","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"24_CR19","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1145\/37402.37406","volume":"21","author":"CW Reynolds","year":"1987","unstructured":"Reynolds, C.W.: Flocks, herds and schools: A distributed behavioral model. ACM Siggraph Comput. Graph. 21(4), 25\u201334 (1987)","journal-title":"ACM Siggraph Comput. Graph."},{"issue":"1","key":"24_CR20","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.ejor.2011.09.031","volume":"218","author":"W Verbeke","year":"2012","unstructured":"Verbeke, W., Dejaeger, K., Martens, D., Hur, J., Baesens, B.: New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. Eur. J. Oper. Res. 218(1), 211\u2013229 (2012)","journal-title":"Eur. J. Oper. Res."},{"unstructured":"Vogel, D., Guyon, I.: Kdd cup 2009: Customer relationship prediction","key":"24_CR21"},{"doi-asserted-by":"crossref","unstructured":"Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases. In: ACM SIGMOD Record, vol. 25, pp. 103\u2013114. ACM (1996)","key":"24_CR22","DOI":"10.1145\/235968.233324"},{"doi-asserted-by":"crossref","unstructured":"Zhao, J., Dang, X.H.: Bank customer churn prediction based on support vector machine: taking a commercial bank\u2019s vip customer churn as the example. In: 4th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, pp. 1\u20134. IEEE (2008)","key":"24_CR23","DOI":"10.1109\/WiCom.2008.2509"}],"container-title":["Lecture Notes in Computer Science","Discovery Science"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-24282-8_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,31]],"date-time":"2019-05-31T03:42:33Z","timestamp":1559274153000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-24282-8_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"ISBN":["9783319242811","9783319242828"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-24282-8_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2015]]}}}