Clustering Based Approach

Clustering-based approaches are computational methods that group similar data points together, aiming to uncover underlying structures and patterns within datasets. Current research focuses on applying these techniques across diverse fields, employing algorithms like k-means and DBSCAN, and exploring model-based clustering for improved performance. These methods are proving valuable for automating laborious tasks (e.g., coin analysis, data curation), enhancing machine learning models (e.g., self-supervised learning, reinforcement learning explainability), and improving predictions in various domains (e.g., time-series forecasting, link prediction in social networks). The resulting insights and improved efficiency have significant implications for both scientific understanding and practical applications.