Cluster-based Data Reduction for Persistent Homology

This peer-reviewed conference publication was produced as part of graduate research conducted at the University of Cincinnati College of Engineering and Applied Science.

The paper investigates clustering techniques as a mechanism for reducing computational complexity in persistent homology workflows. By identifying representative structures within large datasets, the method decreases processing requirements while maintaining topological fidelity.

The work contributed to a broader research effort focused on making topological data analysis computationally feasible for increasingly large datasets.