Customer segmentation using supervised clustering with association rules
Dissertant
Thesis advisor
Harrud, Hamid
Berrado, Abd al-Aziz
Comitee Members
University
Al Akhawayn University
Faculty
School of Science and Engineering
Department
Software Engineering
University Country
Morocco
Degree
Master
Degree Date
2008
English Abstract
In today’s competitive environment, many organizations are focusing on the notion of customer satisfaction and loyalty.
Customer Relationship Management is becoming a common and important concept in many industries.
CRM is a process companies use to understand their customer groups and respond quickly to shifting customer needs and behaviors.
One of the important approaches in CRM that help firms understand their customers is Customer Segmentation.
Segmentation is partitioning a population of customers into different groups that share similar characteristics.
The Clustering technique is usually used for this task.
Clustering algorithms partition data sets into groups of objects such that the pairwise similarity between objects within the same cluster is higher than those assigned to different clusters.
Since customer data contains categorical values, defining a similarity measure becomes challenging and affects the quality and meaningfulness of the clusters formed.
Furthermore, the curse of dimensionality diminishes the robustness of such measures.
Recent research [1] has resulted in a new non-traditional algorithm for clustering high dimensional categorical data, namely SCAR (Supervised Clustering with Association Rules).
SCAR is robust to the curse of dimensionality, it relies on association rules as an intuitive way to evaluate the similarity between objects and group them.
The proposed algorithm has an optional step for further grouping of the clusters formed initially.
In this project, we introduce an implementation of this new algorithm and use it to segment survey data collected to understand the variables that affect purchase intentions in Moroccan large retailers.
Main Subjects
Information Technology and Computer Science
No. of Pages
106
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature review.
Chapter Three : Conceptual analysis.
Chapter Four : Supervised clustering with association rules.
Chapter Five : Project implementation.
Chapter Six : Case study.
Chapter Seven : Conclusion.
References.
American Psychological Association (APA)
Bin Lamlih, Nadiyah. (2008). Customer segmentation using supervised clustering with association rules. (Master's theses Theses and Dissertations Master). Al Akhawayn University, Morocco
https://search.emarefa.net/detail/BIM-629857
Modern Language Association (MLA)
Bin Lamlih, Nadiyah. Customer segmentation using supervised clustering with association rules. (Master's theses Theses and Dissertations Master). Al Akhawayn University. (2008).
https://search.emarefa.net/detail/BIM-629857
American Medical Association (AMA)
Bin Lamlih, Nadiyah. (2008). Customer segmentation using supervised clustering with association rules. (Master's theses Theses and Dissertations Master). Al Akhawayn University, Morocco
https://search.emarefa.net/detail/BIM-629857
Language
English
Data Type
Arab Theses
Record ID
BIM-629857
