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A general framework for distributed approximate similarity search with arbitrary distances
Similarity search is a central problem in domains such as information management and retrieval or data analysis.
Unconventional application of k-means for distributed approximate similarity search
In this paper, we introduce MASK (Multilevel Approximate Similarity search with $k$-means), an unconventional application of the $k$-means algorithm as the foundation of a multilevel index structure for approximate similarity search, suitable for metric spaces.
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