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A Parametrizable Algorithm for Distributed Approximate Similarity Search with Arbitrary Distances
Elena Garcia-Morato, Maria Jesus Algar, Cesar Alfaro, Felipe Ortega, Javier Gomez, Javier M. Moguerza
Recent studies have explored alternative distance measures for similarity search in spaces with diverse topologies, emphasizing the importance of selecting an appropriate distance function to improve the performance of k-Nearest Neighbour search algorithms. However, a critical gap remains in accommodating such diverse similarity measures, as most existing methods for exact or approximate similarity search are explicitly designed for metric spaces. To address this need, we propose PDASC (Parametrizable Distributed Approximate Similarity Search with Clustering), a novel Approximate Nearest Neighbour search algorithm. PDASC combines an innovative multilevel indexing structure particularly adept at managing outliers, highly imbalanced datasets, and sparse data distributions, with the flexibility to support arbitrary distance functions achieved through the integration of clustering algorithms that inherently accommodate them. Experimental results show that PDASC constitutes a reliable ANN search method, suitable for operating in distributed data environments and for handling datasets defined in different topologies, where the selection of the most appropriate distance function is often non-trivial.
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