Inlämning av Examensarbete / Submission of Thesis

Chen Xiaojun; Premlal Bhattrai MCS-2011-19, pp. 78. COM/School of Computing, 2011.

The work

Författare / Author: Chen Xiaojun, Premlal Bhattrai
cxj1022@gmail.com, bhattraiprem@gmail.com
Titel / Title: A Method for Membership Card Generation Based on Clustering and Optimization Models in A Hypermarket
Abstrakt Abstract:

Context: Data mining as a technique is used to find interesting and valuable knowledge from huge amount of stored data within databases or data warehouses. It encompasses classification, clustering, association rule learning, etc., whose goals are to improve commercial decisions and behaviors in organizations. Amongst these, hierarchical clustering method is commonly used in data selection preprocessing step for customer segmentation in business enterprises. However, this method could not treat with the overlapped or diverse clusters very well. Thus, we attempt to combine clustering and optimization into an integrated and sequential approach that can substantially be employed for segmenting customers and subsequent membership cards generation. Clustering methods is used to segment customers into groups while optimization aids in generating the required membership cards.

Objectives: Our master thesis project aims to develop a methodological approach for customer segmentation based on their characteristics in order to define membership cards based on mathematical optimization model in a hypermarket.

Methods: In this thesis, literature review of articles was conducted using five reputed databases: IEEE, Google Scholar, Science Direct, Springer and Engineering Village. This was done to have a background study and to gain knowledge about the current research in the field of clustering and optimization based method for membership card generating in a hypermarket. Further, we also employed video interviews as research methodologies and a proof-of-concept implementation for our solution. Interviews allowed us to collect raw data from the hypermarket while testing the data produces preliminary results. This was important because the data could be regarded as a guideline to evaluate the performance of customer segmentation and generating membership cards.

Results: We built clustering and optimization models as a two-step sequential method. In the first step, the clustering model was used to segment customers into different clusters. In the second step, our optimization model was utilized to produce different types of membership cards. Besides, we tested a dataset consisting of 100 customer records consequently obtaining five clusters and five types of membership cards respectively.

Conclusions: This research provides a basis for customer segmentation and generating membership cards in a hypermarket by way of data mining techniques and optimization. Thus, through our research, an integrated and sequential approach to clustering and optimization can suitably be used for customer segmentation and membership card generation respectively.

Ämnesord / Subject: Datavetenskap - Computer Science\Distributed Computing
Datavetenskap - Computer Science\Software Engineering
Datavetenskap - Computer Science\Artificial Intelligence
Nyckelord / Keywords: Data mining, Hierarchical clustering, Fuzzy clustering, Optimization model, Membership card

Publication info

Dokument id / Document id: houn-8ptlqh
Program:/ Programme Datavetenskapligt program/Computer Science
Registreringsdatum / Date of registration: 12/23/2011
Uppsatstyp / Type of thesis: Masterarbete/Master's Thesis (120 credits)

Context

Handledare / Supervisor: Johan Holmgren
johan.holmgren@bth.se
Examinator / Examiner: Lars Lundberg
Organisation / Organisation: Blekinge Institute of Technology
Institution / School: COM/School of Computing

+46 455 38 50 00

Files & Access

Bifogad uppsats fil(er) / Files attached: bth2011chen.pdf (2933 kB, öppnas i nytt fönster)