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Please use this identifier to cite or link to this item: https://ir.iimcal.ac.in:8443/jspui/handle/123456789/1736
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dc.contributor.authorBose, Indranil
dc.contributor.authorChen, Xi
dc.date.accessioned2021-08-26T06:23:48Z-
dc.date.available2021-08-26T06:23:48Z-
dc.date.issued2014
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84911886787&doi=10.1109%2fiEECON.2014.6925923&partnerID=40&md5=7aae61a672550a5cc2518a863e1ca2a9
dc.identifier.urihttps://ir.iimcal.ac.in:8443/jspui/handle/123456789/1736-
dc.descriptionBose, Indranil, Indian Institute of Management Calcutta, Kolkata, India; Chen, Xi, Zhejiang University, Hangzhou, China
dc.descriptionISSN/ISBN - 978-147993174-3
dc.descriptionDOI - 10.1109/iEECON.2014.6925923
dc.description.abstractExtant research has studied customer behavior in a static manner. But customer clustering can be used to identify the dynamic behavioral patterns of customers over a period of time. We develop a method for extending the standard fuzzy c-means clustering algorithm for detection of temporal changes in customer data. The study using real-life data leads to detection of appearance of new clusters and disappearance of old clusters. Using cluster validity indexes the novel method is shown to lead to formation of clusters that are better than those produced by the fuzzy c-means (FCM) algorithm. © 2014 IEEE.
dc.publisherSCOPUS
dc.publisher2014 International Electrical Engineering Congress, iEECON 2014
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectClusters
dc.subjectFuzzy c-means algorithm
dc.subjectRevenue
dc.subjectTemporal data
dc.subjectUsage
dc.subjectValidity index
dc.titleDetecting temporal changes in customer behavior
dc.typeConference Paper
Appears in Collections:Management Information Systems

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