An optimization of the dynamic set of clusters for similarity searches - Samat, Pablo (2025)
While metric space searching offers a promising solution for efficient similarity searches, the approach lags behind traditional databases in several key areas. A primary weakness is that most indexing methods are static; very few can handle dynamic insertions and deletions cost-effectively without degrading performance. Additionally, even fewer schemes are optimized to operate efficiently on secondary storage. The Dynamic Set of Clusters (DSC) is a dynamic index built for secondary memory, but its performance is hindered by a lack of cluster compactness. To overcome this, we propose an optimization that uses "cut regions” established by global pivots, to more accurately define the data-containing zones within each cluster. This enhanced method is competitive with state-of-the-art approaches, performs well in secondary memory, and serves as a practical alternative for numerous database applications.