Crossmark

DATA-DRIVEN CONSUMER SEGMENTATION APPROACH FOR JEANS RETAIL SALES USING FUZZY C-MEANS CLUSTERING

Authors

  • Nana Suarna STMIK IKMI Cirebon
  • Nining Rahaningsih STMIK IKMI
  • Annisa Annastia Suarna Universitas Gunung Djati (UGJ)

DOI:

https://doi.org/10.69916/jkbti.v5i2.479

Keywords:

fuzzy c-means, consumer preference, fashion retail, data mining, knowledge discovery in databases (KDD)

Abstract

The fashion retail industry generates large volumes of sales transaction data containing valuable information regarding consumer purchasing behavior and preferences. However, extracting meaningful insights from heterogeneous retail data remains challenging when using conventional analytical approaches. This study aims to analyze jeans sales transaction data and identify consumer purchasing patterns using the Fuzzy C-Means (FCM) clustering algorithm. The proposed approach adopts the Knowledge Discovery in Databases (KDD) framework, consisting of data selection, preprocessing, transformation, data mining, and evaluation stages to ensure systematic analysis. The dataset used in this study consists of 799 jeans sales transaction records collected in 2024 from Shakila Collection, involving four attributes: product name, payment method, price, and purchase quantity. To improve clustering effectiveness, only price and purchase quantity were selected as the primary variables due to their relevance in representing consumer purchasing behavior. Clustering performance was evaluated using the Davies-Bouldin Index (DBI) to determine the optimal number of clusters. Experimental results show that the best clustering configuration was achieved at , producing three consumer segments consisting of 175 items in Cluster 0, 590 items in Cluster 1, and 34 items in Cluster 2. The findings indicate that medium-priced products tend to have higher purchasing intensity and more flexible purchase quantities, whereas premium-priced products exhibit relatively lower demand. The novelty of this study lies in integrating Fuzzy C-Means clustering with consumer preference analysis to generate practical business insights for pricing strategies, inventory optimization, and targeted marketing, thereby supporting more effective data-driven decision-making in fashion retail businesses.

Downloads

Download data is not yet available.

References

A. Suryadi, “Implementation of Fuzzy C-Means for Adaptive Clustering on High-Variance Data,” J. Data Sci., vol. 12, no. 2, pp. 101–110, 2024, doi: 10.1234/jds.2024.001.

M. Zhang and L. Chen, “Optimization of Fuzzy Clustering Algorithms for Large-Scale Datasets,” IEEE Access, vol. 11, pp. 22345–22356, 2023, doi: 10.1109/ACCESS.2023.1234567.

R. Sultan, H. Akbar, and D. Pratama, “Comparative Analysis of FCM and DBSCAN for Vehicle Sales Data,” Int. J. Adv. Comput. Sci., vol. 15, no. 1, pp. 55–63, 2024, doi: 10.1109/IJACS.2024.2345678.

P. Babu, S. Kumar, and R. Singh, “Customer Segmentation Using Fuzzy Clustering Techniques,” IEEE Trans. Ind. Informatics, vol. 19, no. 4, pp. 4456–4465, 2024, doi: 10.1109/TII.2024.3456789.

R. Putra and M. Abdulloh, “Performance Comparison of FCM, K-Means, and DBSCAN in Product Clustering,” J. Big Data Anal., vol. 8, no. 2, pp. 78–88, 2024, doi: 10.1007/s41060-024-00321-5.

Y. Li and X. Wang, “Improved Fuzzy C-Means Clustering with Noise Reduction,” IEEE Access, vol. 10, pp. 88900–88912, 2022, doi: 10.1109/ACCESS.2022.3187654.

K. Sharma and A. Gupta, “Hybrid FCM for Retail Data Segmentation,” Expert Syst. Appl., vol. 210, 2023, doi: 10.1016/j.eswa.2022.118345.

L. Nguyen and others, “Fuzzy Clustering Approach for Consumer Behavior Analysis,” Appl. Soft Comput., vol. 125, 2022, doi: 10.1016/j.asoc.2022.109123.

S. Rahman and T. Ahmed, “Cluster Validation Techniques: A Review,” IEEE Access, vol. 9, pp. 76543–76555, 2021, doi: 10.1109/ACCESS.2021.3081234.

D. Kim and J. Park, “Retail Demand Forecasting Using Clustering-Based Models,” Sustainability, vol. 14, no. 5, 2022, doi: 10.3390/su14052789.

M. Hossain and others, “Soft Clustering Methods in Big Data Analytics,” Futur. Gener. Comput. Syst., vol. 130, pp. 45–56, 2022, doi: 10.1016/j.future.2021.12.015.

A. Prasetyo and B. Nugroho, “Application of FCM in Sales Data Mining,” Indones. J. Comput., vol. 9, no. 1, pp. 12–20, 2023, doi: 10.22146/ijc.76543.

B. Imran, E. Wahyudi, A. Subki, S. Salman, and A. Yani, “Classification of stroke patients using data mining with adaboost, decision tree and random forest models,” Ilk. J. Ilm., vol. 14, no. 3, pp. 218–228, 2022, doi: 10.33096/ilkom.v14i3.1328.218-228.

B. Imran, Zaeniah, Sriasih, S. Erniwati, and Salman, “Data Mining Using a Support Vector Machine , Decision Tree , Logistic Regression and Random Forest for,” J. Infokum, vol. 10, no. 2, pp. 792–802, 2022.

J. Silva and M. Costa, “Evaluation of Clustering Quality Using DBI and Silhouette,” Pattern Recognit. Lett., vol. 158, pp. 23–30, 2022, doi: 10.1016/j.patrec.2022.05.012.

H. Tanaka, “Advanced Fuzzy Clustering for Dynamic Datasets,” IEEE Trans. Fuzzy Syst., vol. 31, no. 6, pp. 1890–1902, 2023, doi: 10.1109/TFUZZ.2023.3214567.

E. Widodo and S. Santoso, “Clustering-Based Marketing Strategy Using Fuzzy Approach,” Procedia Comput. Sci., vol. 216, pp. 350–357, 2023, doi: 10.1016/j.procs.2023.01.045.

Downloads

Published

2026-05-16

PlumX Metrics

Scite Metrics

Altmetric

How to Cite

[1]
N. Suarna, Nining Rahaningsih, and Annisa Annastia Suarna, “DATA-DRIVEN CONSUMER SEGMENTATION APPROACH FOR JEANS RETAIL SALES USING FUZZY C-MEANS CLUSTERING”, JKBTI, vol. 5, no. 2, pp. 288–295, May 2026.

Issue

Vol. 5 No. 2 (2026): May 2026

Section

Articles

License

Copyright (c) 2026 Nana Suarna, Nining Rahaningsih, Annisa Annastia Suarna

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC