Performance Evaluation of DBSCAN With Similarity Join Algorithms

Clustering is an important Data Mining operation that groups objects into clusters based on their similarity. The similarity join is a primitive operation used in clustering which retrieves the most similar pairs from two input data-sets based on a dissimilarity function (also named metric). In this article, we transform DBSCAN's (Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with noise) algorithmic schema by replacing the multiple range queries with a single similarity join to minimize the hyperparameter. Thus, instead of the two hyperparameters required by the DBSCAN, our approach requires only the neighborhood radius epsilon hyperparameter. We propose two implementations for DBSCAN with similarity join: i) QuickDBSCAN that uses an adapted QuickJoin algorithm and ii) KDTreeDBSCAN that uses k-d-tree indexing structure. The experimental results show that DBSCAN with similarity join outperforms the classic DBSCAN.