A combination of forensic-based investigation algorithm and density peak-based fuzzy clustering for custom segmentation

https://doi.org/10.31130/ud-jst.2023.081E
Tóm tắt: 64 | PDF: 75

##plugins.themes.academic_pro.article.main##

Author

  • Thi Phuong Quyen Nguyen
    The University of Danang - University of Science and Technology, Danang, Vietnam

Từ khóa:

Clustering method custom segmentation
forensic-based investigation
fuzzy c-means

Tóm tắt

Custom segmentation is a process of classifying potential customers based on their mutual features such as shopping habits, consumption trends, and demand to provide an effective marketing campaign for each customer group. Data clustering is one of the most common methods for custom segmentation. This study proposed a novel clustering method that employs density peak-based fuzzy c-means (DP-FCM) and forensic-based investigation (FBI) algorithms. The proposed method (denoted as DP-FBI-FCM) aims to provide an effective clustering technique that can exploit the global optimal solution for custom segmentation problems. Besides, the proposed DP-FBI-FCM is used to segment wholesale customer data of a supermarket. As a result, four distinct customer groups are classified. Businesses can implement different strategies in each cluster to retain and attract their customers.

Tài liệu tham khảo

    [1] Ahani, M. Nilashi, O. Ibrahim, L. Sanzogni, and S. Weaven, "Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews”, International Journal of Hospitality Management, vol. 80, pp. 52-77, 2019.
    [2] Müller and U. Hamm, "Stability of market segmentation with cluster analysis–A methodological approach”, Food Quality and Preference, vol. 34, pp. 70-78, 2014.
    [3] Zhou et al., "Market segmentation approach to investigate existing and potential aviation markets”, Transport Policy, vol. 99, pp. 120-135, 2020.
    [4] T. Nduna and C. van Zyl, "A benefit segmentation framework for a nature-based tourism destination: the case of Kruger, Panorama and Lowveld areas in Mpumalanga Province”, International Journal of Tourism Cities, vol. 6, no. 4, pp. 953-973, 2020.
    [5] Choi, B. Meng, and T. J. Lee, "An investigation into the segmentation of Japanese traditional ‘Ryokan’hotels using selection attributes”, Journal of Vacation Marketing, vol. 24, no. 4, pp. 324-339, 2018.
    [6] Nduna and C. Van Zyl, "A benefit segmentation analysis of tourists visiting Mpumalanga”, African Journal of Hospitality, Tourism, and Leisure, vol. 6, no. 3, pp. 1-22, 2017.
    [7] Calvo-Porral and J.-P. Lévy-Mangin, "An emotion-based segmentation of bank service customers”, International Journal of Bank Marketing, vol. 38, no. 7, pp. 1441-1463, 2020.
    [8] Machauer and S. Morgner, "Segmentation of bank customers by expected benefits and attitudes”, International Journal of Bank Marketing, vol. 19, no. 1, pp. 6-18, 2001.
    [9] Fang and H. Liu, "Research and Application of Improved Clustering Algorithm in Retail Customer Classification”, Symmetry, vol. 13, no. 10, pp. 1789-1793, 2021.
    [10] Tomassa and E. Rachmawati, "Developing a Marketing Strategy Based on Market Segmentation Through Clustering Method in Food and Beverage Industry in Bandung: Case Study of Bola Ubi Yama”, KnE Social Sciences, 2020, pp. 1080–1105.
    [11] Q. Brito, C. Soares, S. Almeida, A. Monte, and M. Byvoet, "Customer segmentation in a large database of an online customized fashion business”, Robotics and Computer-Integrated Manufacturing, vol. 36, pp. 93-100, 2015.
    [12] Anitha and M. M. Patil, "RFM model for customer purchase behavior using K-Means algorithm”, Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 5, pp. 1785-1792, 2022.
    [13] Aryuni, E. D. Madyatmadja, and E. Miranda, "Customer segmentation in XYZ bank using K-means and K-medoids clustering”, in 2018 International conference on information management and technology (ICIMTech), 2018, pp. 412-416.
    [14] Budayan, I. Dikmen, and M. T. Birgonul, "Comparing the performance of traditional cluster analysis, self-organizing maps and fuzzy C-means method for strategic grouping”, Expert Systems with Applications, vol. 36, no. 9, pp. 11772-11781, 2009.
    [15] P. P. Yuliari, I. K. G. D. Putra, and N. K. D. Rusjayanti, "Customer segmentation through fuzzy C-means and fuzzy RFM method”, Journal of Theoretical and Applied Information Technology, vol. 78, no. 3, pp. 380, 2015.
    [16] Velmurugan, "Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data”, Applied Soft Computing, vol. 19, pp. 134-146, 2014.
    [17] Suthar, P. Indr, and P. Vinit, "A technical survey on DBSCAN clustering algorithm”, Int. J. Sci. Eng. Res, vol. 4, no. 5, pp. 1775-1781, 2013.
    [18] -y. Liu, J.-c. Fan, and Z.-w. Chen, "Improved fuzzy C-means algorithm based on density peak”, International Journal of Machine Learning and Cybernetics, vol. 11, pp. 545-552, 2020.
    [19] Tong, S. Liu, and X.-Z. Gao, "A density-peak-based clustering algorithm of automatically determining the number of clusters”, Neurocomputing, vol. 458, pp. 655-666, 2021.
    [20] Ghezelbash, A. Maghsoudi, and E. J. M. Carranza, "Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm”, Computers & Geosciences, vol. 134, pp. 104335, 2020.
    [21] Zhu, "Safety detection algorithm in sensor network based on ant colony optimization with improved multiple clustering algorithms”, Safety science, vol. 118, pp. 96-102, 2019.
    [22] Li, H. He, and Y. Wen, "Dynamic particle swarm optimization and K-means clustering algorithm for image segmentation”, Optik, vol. 126, no. 24, pp. 4817-4822, 2015.
    [23] -S. Chou and N.-M. Nguyen, "FBI inspired meta-optimization”, Applied Soft Computing, vol. 93, pp. 106339, 2020.
    [24] C. Bezdek, R. Ehrlich, and W. Full, "FCM: The fuzzy c-means clustering algorithm”, Computers and geosciences, vol. 10, no. 2-3, pp. 191-203, 1984.
    [25] Rodriguez and A. Laio, "Clustering by fast search and find of density peaks”, Science, vol. 344, no. 6191, pp. 1492-1496, 2014.
    [26] Du, S. Ding, X. Xu, and Y. Xue, "Density peaks clustering using geodesic distances”, International Journal of Machine Learning and Cybernetics, vol. 9, pp. 1335-1349, 2018.
    [27] "Wholesale customers Data Set", UCI machine learning repository, 2014, [Online]. Availabe: https://archive.ics.uci.edu/ml/
    datasets/wholesale+customers, [Accessed: Jan. 10, 2023].
    [28] Liu, Y., et al. "Understanding of internal clustering validation measures". in 2010 IEEE international conference on data mining, 2010, pp. 911-916.
Xem thêm

##plugins.themes.academic_pro.article.sidebar##

Đã Xuất bản

Jun 30, 2023
Download
PDF (English)
Cách trích dẫn
Nguyen, T. P. Q. “A Combination of Forensic-Based Investigation Algorithm and Density Peak-Based Fuzzy Clustering for Custom Segmentation”. Tạp Chí Khoa học Và Công nghệ - Đại học Đà Nẵng, vol 21, số p.h 6.1, Tháng Sáu 2023, tr 1-6, doi:10.31130/ud-jst.2023.081E.

##plugins.themes.academic_pro.article.details##