Scenario-Based Association Rule Mining in Veterinary Services Using FP-Growth: Differentiating Clinical and Customer-Driven Patterns
DOI:
https://doi.org/10.30871/jaic.v9i3.9698Keywords:
Association Rule Mining, Customer-Driven Patterns, FP-Growth, Procedural Services, Veterinary ClinicAbstract
Veterinary clinics routinely generate transactional data that contain valuable information about both operational workflows and customer preferences. This study aims to differentiate between procedural and customer-driven service patterns by applying the FP-Growth association rule mining algorithm to 1,000 anonymized transactions comprising 94 unique items, collected from a veterinary clinic in West Java, Indonesia, during 2023. Two distinct analytical scenarios were constructed: Scenario 1 includes all services (procedural and customer-driven), while Scenario 2 excludes procedural items such as “Vet” and “Visit Dokter” to focus solely on client-initiated behaviors. Data preprocessing involved aggregating transaction items into a market basket format suitable for frequent pattern mining. The FP-Growth algorithm was employed to extract association rules, evaluated using support, confidence, and lift metrics. Results from Scenario 1 revealed rule patterns reflective of standard clinical protocols and operational dependencies, informing bundled service packages and inventory management. In contrast, Scenario 2 uncovered customer-driven associations, highlighting opportunities for personalized promotions and service innovation. The comparative analysis demonstrates the utility of scenario-based association rule mining for both operational optimization and customer engagement. While the findings provide actionable insights for clinic management, further validation with practitioners and implementation in multi-clinic settings are recommended to confirm real-world applicability and enhance generalizability.
Downloads
References
[1] J. Wang and Z. Cheng, “FP-Growth based Regular Behaviors Auditing in Electric Management Information System,” Procedia Comput. Sci., vol. 139, pp. 275–279, 2018, doi: 10.1016/j.procs.2018.10.268.
[2] S. M. Shah, M. Lütjen, and M. Freitag, “Text mining for supply chain risk management in the apparel industry,” Appl. Sci., vol. 11, no. 5, pp. 1–20, 2021, doi: 10.3390/app11052323.
[3] K. Shaukat, S. Zaheer, and I. Nawaz, “Association Rule Mining: An Application Perspective,” Int. J. Comput. Sci. Innov., no. November 2015, pp. 29–38, 2015.
[4] A. Lawi, A. Agung, D. Ely, and I. Muhammad, “Exploring the Landscape of Deep Learning Techniques for IoT Data : A Systematic Literature Review,” vol. 15, no. 2, pp. 578–588, 2025.
[5] A. E. Widjaja, “Analysis of Apriori and FP-Growth Algorithms for Market Basket Insights: A Case Study of The Bread Basket Bakery Sales,” J. Digit. Mark. Digit. Curr., vol. 1, no. 1, pp. 44–59, 2024, doi: 10.47738/jdmdc.v1i1.2.
[6] A. Senthilkumar, “An efficient FP-Growth based association rule mining algorithm using Hadoop MapReduce,” Indian J. Sci. Technol., vol. 13, no. 34, pp. 3561–3571, 2020, doi: 10.17485/ijst/v13i34.1078.
[7] Iyad S. Alkronz and Samy S. Abu-Naser, “Association Rule Mining using FP-Growth Algorithm in Market Basket Analysis,” Int. J. Acad. Eng. Res., vol. 5, no. 11, pp. 5–13, 2021.
[8] A. A. Dermawan, A. Lawi, D. A. Putera, and D. E. Kurniawan, “Optimizing Inventory Management : Data-Driven Insights from K- Means Clustering Analysis of Prescription Patterns,” vol. 11, no. 3, pp. 811–820, 2024, doi: 10.15294/sji.v11i3.8690.
[9] T. C. Hsiao, S. L. Shieh, T. L. Chen, C. H. Liu, and Y. C. Yeh, “Data analysis of medical records in veterinary hospital using clustering method and association rule,” Appl. Math. Inf. Sci., vol. 9, no. 6, pp. 3319–3326, 2015, doi: 10.12785/amis/090659.
[10] R. Dio, A. A. Dermawan, and D. A. Putera, “Application of Market Basket Analysis on Beauty Clinic to Increasing Customer’s Buying Decision,” Sinkron, vol. 8, no. 3, pp. 1348–1356, 2023, doi: 10.33395/sinkron.v8i3.12421.
[11] G. Schuh et al., “Data mining definitions and applications for the management of production complexity,” Procedia CIRP, vol. 81, no. March, pp. 874–879, 2019, doi: 10.1016/j.procir.2019.03.217.
[12] S. C. Gamoura, D. Damand, Y. Lahrichi, and T. Saikouk, “Jo ur na l P re r f,” Int. J. Prod. Econ., p. 109672, 2025, doi: 10.1016/j.ijpe.2025.109672.
[13] S. Bagui, K. Devulapalli, and J. Coffey, “A heuristic approach for load balancing the FP-growth algorithm on MapReduce,” Array, vol. 7, no. May, p. 100035, 2020, doi: 10.1016/j.array.2020.100035.
[14] C. W. Lin, T. P. Hong, and W. H. Lu, “Linguistic data mining with fuzzy FP-trees,” Expert Syst. Appl., vol. 37, no. 6, pp. 4560–4567, 2010, doi: 10.1016/j.eswa.2009.12.052.
[15] X. Wu, Q. Wen, and J. Zhu, “Association rule mining with a special rule coding and dynamic genetic algorithm for air quality impact factors in Beijing, China,” PLoS One, vol. 19, no. 3 March, pp. 1–23, 2024, doi: 10.1371/journal.pone.0299865.
[16] N. Salsabila, N. Sulistiyowati, and T. N. Padilah, “Pencarian Pola Pemakaian Obat Menggunakan Algoritma FP-Growth,” J. Appl. Informatics Comput., vol. 6, no. 2, pp. 120–128, 2022, doi: 10.30871/jaic.v6i2.4187.
[17] A. Srivastava and D. Sinha, “FP-growth-based signature extraction and unknown variants of DoS/DDoS attack detection on real-time data stream,” J. Inf. Secur. Appl., vol. 89, no. February, p. 103996, 2025, doi: 10.1016/j.jisa.2025.103996.
[18] A. A. Dermawan and M. Muqimuddin, “Strengthening the Competitiveness of SMEs after the Covid 19 Pandemic (Case Study: Producers of Luti Gendang),” … J. Sains, Teknol. dan …, vol. 20, no. 1, pp. 355–364, 2022.
[19] Mitha Rosadi and Muhammad Siddik Hasibuan, “Comparison of Apriori and FP-Growth Algorithms in Analyzing Association Rules,” PIKSEL Penelit. Ilmu Komput. Sist. Embed. Log., vol. 12, no. 2, pp. 399–408, Sep. 2024, doi: 10.33558/piksel.v12i2.9965.
[20] W. NG, R. Sukma, and C. Juliane, “Optimizing Marketing Strategies Using FP-Growth and Association Rule Mining Algorithms in the Textile Industry,” J. World Sci., vol. 3, no. 5, pp. 557–566, 2024, doi: 10.58344/jws.v3i5.599.
[21] B. C. Hidayanto, R. F. Muhammad, R. P. Kusumawardani, and A. Syafaat, “Network Intrusion Detection Systems Analysis using Frequent Item Set Mining Algorithm FP-Max and Apriori,” Procedia Comput. Sci., vol. 124, pp. 751–758, 2017, doi: 10.1016/j.procs.2017.12.214.
[22] M. Yin, W. Wang, Y. Liu, and D. Jiang, “An improvement of FP-Growth association rule mining algorithm based on adjacency table,” MATEC Web Conf., vol. 189, pp. 0–6, 2018, doi: 10.1051/matecconf/201818910012.
[23] K. Gadia and K. Bhowmick, “Parallel text mining in multicore systems using FP-tree algorithm,” Procedia Comput. Sci., vol. 45, no. C, pp. 111–117, 2015, doi: 10.1016/j.procs.2015.03.100.
[24] S. A. Ahmed and B. Nath, Modified FP-Growth: An Efficient Frequent Pattern Mining Approach from FP-Tree, vol. 11941 LNCS. Springer International Publishing, 2019. doi: 10.1007/978-3-030-34869-4_6.
[25] L. Shabtay, P. Fournier-Viger, R. Yaari, and I. Dattner, “A guided FP-Growth algorithm for mining multitude-targeted item-sets and class association rules in imbalanced data,” Inf. Sci. (Ny)., vol. 553, pp. 353–375, 2021, doi: 10.1016/j.ins.2020.10.020.
[26] I. Syukra, A. Hidayat, and M. Z. Fauzi, “Implementation of K-Medoids and FP-Growth Algorithms for Grouping and Product Offering Recommendations,” Indones. J. Artif. Intell. Data Min., vol. 2, no. 2, p. 107, 2019, doi: 10.24014/ijaidm.v2i2.8326.
[27] K. Christidis, D. Apostolou, and G. Mentzas, “Exploring Customer Preferences with Probabilistic Topics Models,” Ke.Tu-Darmstadt.De, pp. 1–13, 2010.
[28] R. Qi and X. Guo, “Analysis of Intelligent Energy Saving Strategy of 4G/5G Network Based on FP-Tree,” Procedia Comput. Sci., vol. 198, no. 2021, pp. 486–492, 2021, doi: 10.1016/j.procs.2021.12.274.
[29] M. Narvekar and S. F. Syed, “An optimized algorithm for association rule mining using FP tree,” Procedia Comput. Sci., vol. 45, no. C, pp. 101–110, 2015, doi: 10.1016/j.procs.2015.03.097.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Rafi Dio, Aulia Agung Dermawan, Dwila Sempi Yusiani, Rifaldi Herikson, Andikha Andikha, Dwi Ely Kurniawan, Adyk Marga Raharja
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
