DGBPSO-DBSCAN: An Optimized Clustering Technique Based on Supervised/Unsupervised Text Representation

Abstract

Density-based spatial clustering of noisy applications (DBSCAN), a widely used density-based clustering technique, faces challenges in determining its key parameter, Eps, leading to manual specification and suboptimal clustering outcomes. Additionally, its time complexity poses limitations. This study introduces a novel approach to enhance the algorithm’s performance. We combined the DBSCAN algorithm with another approach, the particle swarm optimization, based on a novel way to represent text, termed a dependency graph; this method is recognized as DGBPSO-DBSCAN (Dependency Graph Based Particle swarm algorithm for DBSCAN). This paper focuses on employing a novel approach for PSO variable upgrading to explore the Eps range more rapidly and effectively. This method offers a selection of informative elements from the text, where the initial groups are derived from the graph-based degree centrality, the PSO method is used to choose the most compelling features, and the DBSCAN algorithm is used for text clustering. The experimental findings indicate that the modified PSO is used to improve DBSCAN. We compared the outcomes of the suggested technique to those of the standard clustering algorithm. According to the assessment criteria MSE, accuracy, precision, recall, and F-measure, our technique has been demonstrated to be superior to the conventional method.

Keywords

• DBSCAN algorithm
• decision tree
• degree centrality
• dependency graph