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Analysis of Customer Behavior using Clustering and Association Rules

by P.isakki Alias Devi, S.p.rajagopalan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 43 - Number 23
Year of Publication: 2012
Authors: P.isakki Alias Devi, S.p.rajagopalan
10.5120/6420-8925

P.isakki Alias Devi, S.p.rajagopalan . Analysis of Customer Behavior using Clustering and Association Rules. International Journal of Computer Applications. 43, 23 ( April 2012), 19-26. DOI=10.5120/6420-8925

@article{ 10.5120/6420-8925,
author = { P.isakki Alias Devi, S.p.rajagopalan },
title = { Analysis of Customer Behavior using Clustering and Association Rules },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 43 },
number = { 23 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 19-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume43/number23/6420-8925/ },
doi = { 10.5120/6420-8925 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:34:06.381788+05:30
%A P.isakki Alias Devi
%A S.p.rajagopalan
%T Analysis of Customer Behavior using Clustering and Association Rules
%J International Journal of Computer Applications
%@ 0975-8887
%V 43
%N 23
%P 19-26
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The analysis of customer behavior is used to maintain good relationship with customers. It maximizes the customer satisfaction. We can also improve customer loyalty and retention. The aim of this paper is to develop a very useful trend for launching products with configurations for customers of different gender based on past transactions. Based on the previous transactions of the customers, prediction is done and data is estimated with the help of clustering and association rules. This paper proposes an effective method to extract knowledge from transactions records which is very useful for increasing the sales. Customer details are segmented using k-means and then Apriori algorithm is applied to identify customer behavior. This is followed by the identification of product associations within segments. This paper aims to develop a new trend and launch a new series of products using the previous transactions of the customers.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Customer Relationship Management Data Mining Clustering Association Rules K-means Apriori Algorithm

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