Combination Grouping Techniques and Association Rules For Marketing Analysis based Customer Segmentation


  • Amir Mahmud Husein Universitas Prima Indonesia
  • Dodi Setiawan
  • Andika Rahmad Kolose Sumangunsong
  • Andreas Simatupang
  • Shela Aura Yasmin




Changes in people's transaction behavior using the internet resulted in the exponential growth of e-commerce. With the growth of digital shopping transactions, it is difficult to predict customer segments and patterns using traditional mathematical models. Timely identification of emerging trends from large volumes of data plays a major role in business processes and decision making. This is different from previous research works that apply the RFM model based on K-Means Clustering to find potential customers as an ingredient in determining marketing targets. In this study, a clustering technique approach is proposed to classify customer data which is evaluated using the Davies Bouldin, Calinski Harabasz and Silhouette methods to determine the optimal number of clusters, then the results are used in the Apriori algorithm to find patterns of goods that are often purchased together. Based on the test results on the K-Means Clustering, Spectral Clustering, and Gaussian Mixture Model techniques produced 5 clusters with 76% more accurate the K-Means Clustering method than the other two methods so that it was determined as a method in the RMF model, then the results of customer grouping were used on the Apriori algorithm to find patterns of concurrent product purchases by customers that are expected to be useful in future marketing management.

GS Cited Analysis


Download data is not yet available.


D. Kamthania, A. Pahwa, and S. S. Madhavan, 2018, “Market segmentation analysis and visualization using K-mode clustering algorithm for E-commerce business,” J. Comput. Inf. Technol., vol. 26, no. 1, pp. 57–68, doi: 10.20532/cit.2018.1003863.

Dedi, M. I. Dzulhaq, K. W. Sari, S. Ramdhan, R. Tullah, and Sutarman, 2019, “Customer Segmentation Based on RFM Value Using K-Means Algorithm,” Proc. 2019 4th Int. Conf. Informatics Comput. ICIC doi: 10.1109/ICIC47613.2019.8985726.

J. Wu et al., 2021, “ User Value Identification Based on Improved RFM Model and K -Means++ Algorithm for Complex Data Analysis ,” Wirel. Commun. Mob. Comput., vol, pp. 1–8, 2021, doi: 10.1155/2021/9982484.

J. Wu et al., 2020, “An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K -Means Algorithm,” Math. Probl. Eng., , doi: 10.1155/2020/8884227.

J. Wu et al., 2020, “An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K -Means Algorithm,” Math. Probl. Eng., vol. , doi: 10.1155/2020/8884227.

M. Pohludka and H. Štverková, 2019, “The Best Practice of CRM Implementation for Small- and Medium-Sized Enterprises,” Adm. Sci., vol. 9, no. 1, p. 22, Mar, doi: 10.3390/admsci9010022.

M. Ryu, K. Il Ahn, and K. Lee, 2021, “Finding effective item assignment plans with weighted item associations using a hybrid genetic algorithm,” Appl. Sci., vol. 11, no. 5, pp. 1–19, Mar, doi: 10.3390/app11052209.

O. Piskunova and R. Klochko, 2020, “Classification of e-commerce customers based on Data Science techniques,” CEUR Workshop Proc., vol. 2649, pp. 6–20.

S. G. Carbajal, 2021, “Customer segmentation through path reconstruction,” Sensors, vol. 21, no. 6, pp. 1–17, Mar. doi: 10.3390/s21062007.

S. Guney, S. Peker, and C. Turhan, 2020, “A combined approach for customer profiling in video on demand services using clustering and association rule mining,” IEEE Access, vol. 8, pp. 84326–84335, doi: 10.1109/ACCESS.2020.2992064.

T. Sai Vijay, S. Prashar, and V. Sahay, 2019, “The influence of online shopping values and web atmospheric cues on e-loyalty: Mediating role of e-satisfaction,” J. Theor. Appl. Electron. Commer. Res., vol. 14, no. 1, pp. 1–15, doi: 10.4067/S0718-18762019000100102.

W. J. Chen, M. J. Jhou, T. S. Lee, and C. J. Lu, 2021, “Hybrid basketball game outcome prediction model by integrating data mining methods for the national basketball association,” Entropy, vol. 23, no. 4, p. 477, Apr, doi: 10.3390/e23040477.

Y. Cheng, M. Cheng, T. Pang, and S. Liu, 2021, “Using Clustering Analysis and Association Rule Technology in Cross-Marketing,” Complexity, vol, doi: 10.1155/2021/9979874.

Y. Han, D. Yu, C. Yin, and Q. Zhao, 2020, “Temporal Association Rule Mining and Updating and Their Application to Blast Furnace in the Steel Industry,” Comput. Intell. Neurosci., vol, doi: 10.1155/2020/7467213.


Crossmark Updates

How to Cite

Husein, A. M., Dodi Setiawan, Andika Rahmad Kolose Sumangunsong, Andreas Simatupang, & Shela Aura Yasmin. (2022). Combination Grouping Techniques and Association Rules For Marketing Analysis based Customer Segmentation. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(3), 1998-2007.

Most read articles by the same author(s)

1 2 > >>