GPU-SME-kNN: Scalable and memory efficient kNN and lazy learning using GPUs

The k nearest neighbor (kNN) rule is one of the most used techniques in data mining and pattern recognition due to its simplicity and low identification error. However, the computational effort it requires is directly related to the dataset sizes, hence delivering a poor performance on large datasets. ::: The :::: use :: of :::::::: graphics processing units (GPU) ::: has :::::::: improved :::: the :::::::: run-time : performance of the kNN rule but the computational requirements of current ::::::::: approaches ::::: limit ::: this : performance as the dataset size increases. In this paper : , : we propose a new scalable and memory efficient design for a GPU-based kNN rule, called GPU-SME-kNN, that ::::: breaks :::: the ::::::::::: dependency between dataset size and memory footprint while delivering high performance. An experimental study of GPU-SME-kNN is presented showing a high performance, even in cases that other :::::::: methods : cannot address, ::::: while ::: the :::::::::::::: computational ::::::::::: requirements :::: are : suitable for most commercial GPU devices. Our design has also been applied to kNN-based lazy learning algorithms reducing run-times in a significant way.