NDLI: The Research of Customer Classification Based on Extended Bayes Model

Content Provider	IEEE Xplore Digital Library
Author	Qiaohong Zu Li Wenfeng
Copyright Year	2008
Description	Author affiliation: Sch. of LogisticsEngineering, WHUT, Wuhan (Qiaohong Zu; Li Wenfeng)
Abstract	Naive Bayes classifier, a classification method based on Bayes theory, shows excellent properties in many fields. In practical application, limited to satisfying independence assumption, it is hard to gain better classification. In this paper an simple and intuitive extended Bayes model was constructed, which can get better classification effect. The extended Bayes model replaces primary attribute group with new attribute group (except categorical attribute) and rearrange weights according to the comparison of the expectation of attribute importance, to reinforce the effect of important attributes and weak the effect of subordinate attributes. The extended Bayes model was applied in customer classification prediction and was veried with examples. Firstly, customers were clustered with K-means algorithm, the cluster result as a pretreatment step was used in customer classification. Prediction by weighted Bayes algorithm, it combines the advantages of the two algorithms to improve the accuracy of customer classification. So a customer segmentation model can be built on a basis of customer lifetime value, customer loyalty degree, client capital credit and etc.
Starting Page	22
Ending Page	25
File Size	1029522
Page Count	4
File Format	PDF
ISBN	9781424420209
DOI	10.1109/ICPCA.2008.4783580
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-06
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Customer Classification Prediction Error analysis Predictive models Probability distribution Asset management Classification algorithms Weighted Bayes Algorithm Clustering algorithms Set theory Frequency Prediction algorithms Extended Bayes Model Testing
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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