The Excellence
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TKDE 2024 paper on optimizing the number of clusters for billion-scale quantization-based nearest neighbor search, providing methods to determine optimal clustering for quantized vector indexing.

An efficient clustering method for billion-scale feature vectors that compresses input vectors into short product-quantized (PQ) codes to achieve fast and memory-efficient clustering. PQk-means can cluster one billion 128D SIFT features in 14 hours using just 32 GB of memory.

A vector indexing technique that partitions the vector space into clusters using k-means, then searches only the nearest clusters during queries. Foundation for efficient approximate nearest neighbor search, often combined with product quantization (IVF-PQ).

Inverted File index with FLAT (uncompressed) vectors, partitioning the vector space into clusters with centroids, offering a balance between search speed and accuracy for approximate nearest neighbor search.

Clustering-based approximate nearest neighbor algorithm that partitions vector space into Voronoi cells. Fast search through coarse-to-fine strategy, often combined with Product Quantization (IVF-PQ).

Parallel Efficient Clustering with graph-based Approximate Nearest Neighbor search, providing efficient clustering algorithms optimized for high-dimensional vector spaces.

Hierarchical Clustering-based Nearest Neighbor Graph using MST to connect dataset points through multiple hierarchical clusters. Performs efficient guided search instead of traditional greedy routing.

K-means Tree is a clustering-based data structure that organizes high-dimensional vectors for fast similarity search and retrieval. It is used as an indexing method in some vector databases to optimize performance for vector search operations.