Scenario-Based Association Rule Mining in Veterinary Services Using FP-Growth: Differentiating Clinical and Customer-Driven Patterns
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 t...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Politeknik Negeri Batam
2025-06-01
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| Series: | Journal of Applied Informatics and Computing |
| Subjects: | |
| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9698 |
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| Summary: | 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. |
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| ISSN: | 2548-6861 |