Utilizing Ensemble Approach for Predictive Customer Clustering Analysis with Unsupervised Cluster Labeling

Micheal  Atunwa; Zia Shamszaman; Shatha Ghareeb; Jamila Mustafina

doi:10.1109/DeSE60595.2023.10469368

Utilizing Ensemble Approach for Predictive Customer Clustering Analysis with Unsupervised Cluster Labeling

Micheal Atunwa, Zia Shamszaman, Shatha Ghareeb, Jamila Mustafina

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Customer clustering is an unsupervised machinelearning approach that groups diverse customers based on shared
characteristics. This research explores the use of machine learning, particularly the k-means model and predictive algorithms,
to improve the precision of customer cluster analysis in the
retail sector. An Ensemble approach is proposed to gain deeper
insights into customer behavior and predict future behavior
within the same clusters. The study evaluates multiple machine
learning techniques, splitting a time series dataset into two
frames, and employs K-fold cross-validation for model performance enhancement. Key evaluation metrics include Accuracy,
Precision, Recall, and F1-score. The findings show that the
Extreme Gradient Boosting Classifier performs best in Dataset
One, while Random Forest outperforms in Dataset Two. The
project aims to combine these top-performing ensemble classifiers
using the Voting Classifier to classify customers. The proposed
model achieves high precision scores for both datasets, making
it a promising approach for customer classification.

Original language	English
Title of host publication	DeSE 2023 - Proceedings
Subtitle of host publication	16th International Conference on Developments in eSystems Engineering
Editors	Dhiya Al-Jumeily Obe, Sulaf Assi, Manoj Jayabalan, Jade Hind, Abir Hussain, Hissam Tawfik, Neil Rowe, Jamila Mustafina
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	258-263
Number of pages	6
ISBN (Electronic)	9798350381344
ISBN (Print)	9798350381344
DOIs	https://doi.org/10.1109/DeSE60595.2023.10469368 https://doi.org/10.1109/DeSE60595.2023.10469368
Publication status	Published - 21 Mar 2024
Event	16th International Conference on Developments in eSystems Engineering - ATLAS University, Istanbul, Turkey Duration: 18 Dec 2023 → 20 Dec 2023 https://dese.ai/dese-2023/

Publication series

Name	Proceedings - International Conference on Developments in eSystems Engineering, DeSE
ISSN (Print)	2161-1343

Conference

Conference	16th International Conference on Developments in eSystems Engineering
Abbreviated title	DeSE 2023
Country/Territory	Turkey
City	Istanbul
Period	18/12/23 → 20/12/23
Internet address	https://dese.ai/dese-2023/

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Access to Document

https://ieeexplore.ieee.org/document/10469368

Cite this

Atunwa, M., Shamszaman, Z., Ghareeb, S., & Mustafina, J. (2024). Utilizing Ensemble Approach for Predictive Customer Clustering Analysis with Unsupervised Cluster Labeling. In D. A.-J. Obe, S. Assi, M. Jayabalan, J. Hind, A. Hussain, H. Tawfik, N. Rowe, & J. Mustafina (Eds.), DeSE 2023 - Proceedings: 16th International Conference on Developments in eSystems Engineering (pp. 258-263). (Proceedings - International Conference on Developments in eSystems Engineering, DeSE). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DeSE60595.2023.10469368, https://doi.org/10.1109/DeSE60595.2023.10469368

Atunwa, Micheal ; Shamszaman, Zia ; Ghareeb, Shatha et al. / Utilizing Ensemble Approach for Predictive Customer Clustering Analysis with Unsupervised Cluster Labeling. DeSE 2023 - Proceedings: 16th International Conference on Developments in eSystems Engineering. editor / Dhiya Al-Jumeily Obe ; Sulaf Assi ; Manoj Jayabalan ; Jade Hind ; Abir Hussain ; Hissam Tawfik ; Neil Rowe ; Jamila Mustafina. Institute of Electrical and Electronics Engineers Inc., 2024. pp. 258-263 (Proceedings - International Conference on Developments in eSystems Engineering, DeSE).

@inproceedings{0e5e6480f79a43c4807e1e2f7708f2ec,

title = "Utilizing Ensemble Approach for Predictive Customer Clustering Analysis with Unsupervised Cluster Labeling",

abstract = "Customer clustering is an unsupervised machinelearning approach that groups diverse customers based on sharedcharacteristics. This research explores the use of machine learning, particularly the k-means model and predictive algorithms,to improve the precision of customer cluster analysis in theretail sector. An Ensemble approach is proposed to gain deeperinsights into customer behavior and predict future behaviorwithin the same clusters. The study evaluates multiple machinelearning techniques, splitting a time series dataset into twoframes, and employs K-fold cross-validation for model performance enhancement. Key evaluation metrics include Accuracy,Precision, Recall, and F1-score. The findings show that theExtreme Gradient Boosting Classifier performs best in DatasetOne, while Random Forest outperforms in Dataset Two. Theproject aims to combine these top-performing ensemble classifiersusing the Voting Classifier to classify customers. The proposedmodel achieves high precision scores for both datasets, makingit a promising approach for customer classification.",

author = "Micheal Atunwa and Zia Shamszaman and Shatha Ghareeb and Jamila Mustafina",

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Atunwa, M, Shamszaman, Z , Ghareeb, S & Mustafina, J 2024, Utilizing Ensemble Approach for Predictive Customer Clustering Analysis with Unsupervised Cluster Labeling. in DA-J Obe, S Assi, M Jayabalan, J Hind, A Hussain, H Tawfik, N Rowe & J Mustafina (eds), DeSE 2023 - Proceedings: 16th International Conference on Developments in eSystems Engineering. Proceedings - International Conference on Developments in eSystems Engineering, DeSE, Institute of Electrical and Electronics Engineers Inc., pp. 258-263, 16th International Conference on Developments in eSystems Engineering, Istanbul, Turkey, 18/12/23. https://doi.org/10.1109/DeSE60595.2023.10469368, https://doi.org/10.1109/DeSE60595.2023.10469368

Utilizing Ensemble Approach for Predictive Customer Clustering Analysis with Unsupervised Cluster Labeling. / Atunwa, Micheal ; Shamszaman, Zia ; Ghareeb, Shatha et al.
DeSE 2023 - Proceedings: 16th International Conference on Developments in eSystems Engineering. ed. / Dhiya Al-Jumeily Obe; Sulaf Assi; Manoj Jayabalan; Jade Hind; Abir Hussain; Hissam Tawfik; Neil Rowe; Jamila Mustafina. Institute of Electrical and Electronics Engineers Inc., 2024. p. 258-263 (Proceedings - International Conference on Developments in eSystems Engineering, DeSE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Utilizing Ensemble Approach for Predictive Customer Clustering Analysis with Unsupervised Cluster Labeling

AU - Atunwa, Micheal

AU - Shamszaman, Zia

AU - Ghareeb, Shatha

AU - Mustafina, Jamila

PY - 2024/3/21

Y1 - 2024/3/21

N2 - Customer clustering is an unsupervised machinelearning approach that groups diverse customers based on sharedcharacteristics. This research explores the use of machine learning, particularly the k-means model and predictive algorithms,to improve the precision of customer cluster analysis in theretail sector. An Ensemble approach is proposed to gain deeperinsights into customer behavior and predict future behaviorwithin the same clusters. The study evaluates multiple machinelearning techniques, splitting a time series dataset into twoframes, and employs K-fold cross-validation for model performance enhancement. Key evaluation metrics include Accuracy,Precision, Recall, and F1-score. The findings show that theExtreme Gradient Boosting Classifier performs best in DatasetOne, while Random Forest outperforms in Dataset Two. Theproject aims to combine these top-performing ensemble classifiersusing the Voting Classifier to classify customers. The proposedmodel achieves high precision scores for both datasets, makingit a promising approach for customer classification.

AB - Customer clustering is an unsupervised machinelearning approach that groups diverse customers based on sharedcharacteristics. This research explores the use of machine learning, particularly the k-means model and predictive algorithms,to improve the precision of customer cluster analysis in theretail sector. An Ensemble approach is proposed to gain deeperinsights into customer behavior and predict future behaviorwithin the same clusters. The study evaluates multiple machinelearning techniques, splitting a time series dataset into twoframes, and employs K-fold cross-validation for model performance enhancement. Key evaluation metrics include Accuracy,Precision, Recall, and F1-score. The findings show that theExtreme Gradient Boosting Classifier performs best in DatasetOne, while Random Forest outperforms in Dataset Two. Theproject aims to combine these top-performing ensemble classifiersusing the Voting Classifier to classify customers. The proposedmodel achieves high precision scores for both datasets, makingit a promising approach for customer classification.

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DO - 10.1109/DeSE60595.2023.10469368

M3 - Conference contribution

AN - SCOPUS:85189333362

SN - 9798350381344

T3 - Proceedings - International Conference on Developments in eSystems Engineering, DeSE

SP - 258

EP - 263

BT - DeSE 2023 - Proceedings

A2 - Obe, Dhiya Al-Jumeily

A2 - Assi, Sulaf

A2 - Jayabalan, Manoj

A2 - Hind, Jade

A2 - Hussain, Abir

A2 - Tawfik, Hissam

A2 - Rowe, Neil

A2 - Mustafina, Jamila

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 16th International Conference on Developments in eSystems Engineering

Y2 - 18 December 2023 through 20 December 2023

ER -

Atunwa M, Shamszaman Z , Ghareeb S, Mustafina J. Utilizing Ensemble Approach for Predictive Customer Clustering Analysis with Unsupervised Cluster Labeling. In Obe DAJ, Assi S, Jayabalan M, Hind J, Hussain A, Tawfik H, Rowe N, Mustafina J, editors, DeSE 2023 - Proceedings: 16th International Conference on Developments in eSystems Engineering. Institute of Electrical and Electronics Engineers Inc. 2024. p. 258-263. (Proceedings - International Conference on Developments in eSystems Engineering, DeSE). doi: 10.1109/DeSE60595.2023.10469368, 10.1109/DeSE60595.2023.10469368

Utilizing Ensemble Approach for Predictive Customer Clustering Analysis with Unsupervised Cluster Labeling

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