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Customer segmentation using supervised clustering with association rules
مقدم أطروحة جامعية
مشرف أطروحة جامعية
Harrud, Hamid
Berrado, Abd al-Aziz
أعضاء اللجنة
الجامعة
جامعة الأخوين
الكلية
كلية الهندسة و العلوم
القسم الأكاديمي
هندسة البرمجيات
دولة الجامعة
المغرب
الدرجة العلمية
ماجستير
تاريخ الدرجة العلمية
2008
الملخص الإنجليزي
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.
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
عدد الصفحات
106
قائمة المحتويات
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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
لغة النص
الإنجليزية
نوع البيانات
رسائل جامعية
رقم السجل
BIM-629857
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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