DBSCAN Parameter Selection Based on K-NN

Leonardo Delgado, Eduardo F. Morales

Abstract: In this paper, we introduce a parameter selection for the algorithm DBSCAN based on the $$K-neighborhood$$ . We change the parameters $$\epsilon $$ and $$min\_points$$ by a $$K-neighborhood$$ (named $$\beta $$ ), scale, and an $$\alpha $$ value. We use the scale parameter to balance the dataset. $$\beta $$ is used to select $$min\_points-\epsilon $$ and $$\alpha $$ to reduce the value of $$min\_points$$ . We use homogeneity, completeness, and v-measure scores over datasets with balanced and unbalanced clusters to evaluate the performance. We compared our results against ACND and DBSCAN with the original parameter selection. Finally, we use our proposal to detect contour over 3D shapes. Our results show better performance in three of the eight datasets, and a better performance into border detection on 3D shapes.