Detecting temporal changes in customer behavior

Please use this identifier to cite or link to this item: https://ir.iimcal.ac.in:8443/jspui/handle/123456789/1736

Title:	Detecting temporal changes in customer behavior
Authors:	Bose, Indranil Chen, Xi
Keywords:	Clusters Fuzzy c-means algorithm Revenue Temporal data Usage Validity index
Issue Date:	2014
Publisher:	SCOPUS 2014 International Electrical Engineering Congress, iEECON 2014 Institute of Electrical and Electronics Engineers Inc.
Abstract:	Extant 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.
Description:	Bose, Indranil, Indian Institute of Management Calcutta, Kolkata, India; Chen, Xi, Zhejiang University, Hangzhou, China ISSN/ISBN - 978-147993174-3 DOI - 10.1109/iEECON.2014.6925923
URI:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-84911886787&doi=10.1109%2fiEECON.2014.6925923&partnerID=40&md5=7aae61a672550a5cc2518a863e1ca2a9 https://ir.iimcal.ac.in:8443/jspui/handle/123456789/1736
Appears in Collections:	Management Information Systems

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