Clustering Astronomical Orbital Synthetic Data Using Advanced Feature Extraction and Dimensionality Reduction Techniques

This research, accepted for publication in Neural Computing and Applications, presents advanced techniques for clustering astronomical orbital synthetic data. It focuses on feature extraction and dimensionality reduction, which are vital for efficiently processing large datasets in Earth and Planetary Astrophysics, with broad implications for future space data analysis.