TQC: An intelligent clustering approach for large-scale, noisy, and imbalanced data

As an unsupervised learning method, clustering is a critical technique in artificial intelligence for organizing raw data into meaningful groups. In this process, data is partitioned based on the internal similarity of members within the same cluster and the maximum external distance from other clusters. Beyond business analytics, healthcare, economics, and other fields, clustering has been widely applied across disciplines. Extracting practical knowledge from large datasets relies on an effective clustering technique. Processing speed, especially for large datasets, handling noisy data and outliers, and ensuring high accuracy are the main challenges in clustering. These problems are especially significant in contemporary applications, where heterogeneous and inherently noisy datasets are prevalent. Combining the Trees Social Relation Algorithm (TSR) with the Queue Learning (QL) algorithm, the proposed approach, TQC (Tree-Queue Clustering), addresses these problems. While the QL algorithm enhances clustering accuracy, the TSR method focuses on accelerating clustering. The suggested approach first divides the data into smaller groups. Then, by effectively computing group memberships, TSR's migration process causes clusters to develop progressively. Handling noise and outliers helps the QL algorithm prevent local optima and improve clustering efficiency. This hybrid approach ensures the formation of high-quality clusters and accelerates convergence. The suggested method is validated across several real-world datasets of varying sizes and properties. Experimental results, evaluated using five performance metrics — MICD, ARI, NMI, ET, and ODR — and compared with eight state-of-the-art algorithms, demonstrate the proposed method's superior performance in both speed and accuracy.