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Marketing Data Mining Classifiers: Criteria Selection Issues in Customer Segmentation

by Masoud Abessi, Elahe Hajigol Yazdi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 106 - Number 10
Year of Publication: 2014
Authors: Masoud Abessi, Elahe Hajigol Yazdi
10.5120/18554-6238

Masoud Abessi, Elahe Hajigol Yazdi . Marketing Data Mining Classifiers: Criteria Selection Issues in Customer Segmentation. International Journal of Computer Applications. 106, 10 ( November 2014), 5-10. DOI=10.5120/18554-6238

@article{ 10.5120/18554-6238,
author = { Masoud Abessi, Elahe Hajigol Yazdi },
title = { Marketing Data Mining Classifiers: Criteria Selection Issues in Customer Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 106 },
number = { 10 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 5-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume106/number10/18554-6238/ },
doi = { 10.5120/18554-6238 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:39:02.194402+05:30
%A Masoud Abessi
%A Elahe Hajigol Yazdi
%T Marketing Data Mining Classifiers: Criteria Selection Issues in Customer Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 106
%N 10
%P 5-10
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining is automated or semi-automated Knowledge Discovery from large amounts of stored data in order to discovering meaningful patterns and rules. Marketing related data mining applied to market segmentation, customer services, credit and behavior scoring, and benchmarking. There are different classifiers including decision tree, ID3, CART, Quest, Neural networks, Association, Bayesian, and etc. In this study, ten classifiers are examined to identify important issues in mining marketing data. Classification accuracy, learning speed, Classification speed, Missing value, and robustness are some indices included to compare and contrast the classifiers. Shopping malls' consumer behavior data were used in our investigation. Results indicate that classifiers perform differently under different consumer data types.

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Index Terms

Computer Science
Information Sciences

Keywords

Data mining Supervised learning algorithm classification techniques

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