Tree and Graph Based Two-Stages Routing for Approximate Nearest Neighbor Search

With the expansion of extensive datasets of high-dimensional vectors, the approximate nearest neighbor search (ANNS) has become increasingly significant in data mining. Among the prevailing ANNS methods, graph-based methods achieve high recall at the cost of excessive memory usage, whereas tree-based methods are memory-efficient but suffer from low recall and increased search complexity. In this paper, we propose a hybrid index based on Ball tree and Hierarchical Navigable Small World(HNSW). Ball-HNSW employs a two-stages hybrid framework to accelerate the identification of approximate nearest neighbors. For datasets with different distributions, we utilize reinforcement learning to optimize parameter combinations by simulating decisions during the search process, thereby improving search results. Experiments confirm that Ball-HNSW outperforms other state-of-the-art methods on multiple datasets.