SS-DBSCAN: Semi-Supervised Density-Based Spatial Clustering of Applications With Noise for Meaningful Clustering in Diverse Density Data

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an unsupervised clustering algorithm designed to identify clusters of various shapes and sizes in noisy datasets by pinpointing core points. The primary challenges associated with the DBSCAN algorithm involve the recognition of meaningful clusters within varying densities datasets and its sensitivity to parameter values of Epsilon distance and minimum number of neighbor points. These two issues may result in merging small clusters into larger clusters or splitting valid clusters into smaller clusters. A new Semi-Supervised DBSCAN (SS-DBSCAN) algorithm is introduced to improve the recognition of meaningful clusters. DBSCAN requires core points to be within, at most, Epsilon distance from their minimum neighboring points. The SS-DBSCAN algorithm, a modified version of the original DBSCAN, adds a pre-specified condition or constraint to identify core points further. This extra constraint is related to the clustering objective of a given dataset. To evaluate the effectiveness of SS-DBSCAN, we utilize three datasets: letter recognition, wireless localization, and Modern Standard Arabic (MSA) combined with Iraqi words language modeling. V-measure is used to evaluate the clustering efficiency for the letters recognition and wireless localization datasets. The perplexity (pp) of the class-based language model, built on the produced clusters, is the metric used for the Iraqi-MSA dataset clustering effectiveness. Experimental results showed the significant effectiveness of SS-DBSCAN. It outperforms DBSCAN when applied to letters and Iraqi-MSA datasets with improvements of 65% and 14.5%, respectively. A comparable performance was achieved when clustering the wireless localization dataset. Additionally, to assess the effectiveness of SS-DBSCAN, its performance has been compared to various modified versions of DBSCAN using four metrics: V-measure, PP, Adjusted Rand Index (ARI), and the Silhouette score. Based on these metrics, the results showed that SS-DBSCAN outperformed most DBSCAN versions in three case studies. Consequently, the proposed SS-DBSCAN algorithm is particularly suitable for high-density datasets. The SS-DBSCAN python code is available at https://github.com/TibaZaki/SS_DBSCAN.