Mining association rules through integration of clustering analysis and ant colony system for health insurance database in Taiwan

In addition to sharing and applying the knowledge in the community, knowledge discovery has become an important issue in the knowledge economic era. Data mining plays an important role of knowledge discovery. Therefore, this study intends to propose a novel framework of data mining which clusters the data first and then followed by association rules mining. The first stage employs the ant system-based clustering algorithm (ASCA) and ant K-means (AK) to cluster the database, while the ant colony system-based association rules mining algorithm is applied to discover the useful rules for each group. The medical database provided by the National Health Insurance Bureau of Taiwan Government is used to verify the proposed method. The evaluation results showed that the proposed method not only is able to extract the rules much faster, but also can discover more important rules.