Global understanding via local extraction for data clustering and visualization

Retrieving latent class patterns from complex data is challenging. This paper focuses on the problem of retrieving latent classes from local connections of raw data without any assumptions regarding data structures or distributions. We propose a framework called GULE (global understanding via local extraction) to address this challenge through both local extraction of class consistency and global propagation of the identified consistency. This paper provides a series of theoretical analyses to show why the GULE algorithm can retrieve latent classes with high accuracy. GULE can also serve as a tool for data visualization to preserve class topology structures. Comprehensive testing demonstrates that GULE provides precise clustering and highly reliable visualizations, potentially offering insights into diverse applications, including biology and medicine.