Scaling KNN Computation over Large Graphs on a PC
Abstract
This paper proposes a novel approach to compute K-Nearest Neighbors (KNN) algorithm on a large set of users by lever-aging disk and memory efficiently on a commodity PC. The system is designed to minimize random accesses to disk as well as the amount of data loaded/unloaded from/to disk so as to better utilize the computational power, thus improving the algorithmic efficiency.
Domains
Computer Science [cs]Origin | Files produced by the author(s) |
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