Comparison of the DBSCAN Algorithm and Affinity Propagation on Business Incubator Tenant Customer Segmentation

The increasingly complex business environment necessitates businesses to design more effective and efficient strategies for company development, including market expansion. To understand customer behaviors, data analysis becomes crucial. One common approach used group customers is segmentation based on RFM (Recency, Frequency, Monetary). This study aims compare the performance of two clustering algorithms, namely DBSCAN Affinity Propagation (AP), in providing profile segment recommendations using analysis. algorithm employed due its ability identify arbitrarily shaped clusters handle noise. On other hand, (AP) chosen capability discover cluster centers without requiring a pre-defined number clusters. transaction dataset this research obtained from one incubator tenants at STIKOM Bali. undergoes preprocessing steps before being segmented both AP algorithms. Performance evaluation algorithms conducted Silhouette Scores Davies-Bouldin Index (DBI) matrices. findings indicate that outperforms case. yields 0.699 DBI 0.429, along with 4 segments. Furthermore, further performed results statistical mean values each variables. four segments generated by algorithm, variables, can be associated concept relationship management.