Active Semi-Supervised Clustering Algorithm for Multi-Density Datasets

Semi-supervised clustering with pairwise constraints has been a hot topic among researchers and experts. However, the problem becomes quite difficult to manage using random constraints for clustering data when the clusters have different shapes, densities, and sizes. This research proposes an active semi-supervised density-based clustering algorithm, termed "ASS-DBSCAN," designed specifically for clustering multi-density data. By integrating active learning and semi- supervised techniques, ASS-DBSCAN enhances traditional clustering methods, allowing it to handle complex data distributions with varying densities more effectively. This research provides two major contributions. The first contribution of this research is to analyze how to link constraints (including that must be linked and ones that should not be linked) that will be utilized by the clustering algorithm. The second contribution made by this research is the ability to add multiple density levels to the dataset. We perform experiments over real datasets. The ASS-DBSCAN algorithm was evaluated against existing state-of-the-art system for various evaluation metrics in which it performed remarkably well.