k-nearest neighbor (kNN) is a widely used learning algorithm for supervised tasks. In practice, the main challenge when using kNN its high sensitivity to hyperparameter setting, including number of nearest neighbors k, distance function, and weighting function. To improve robustness hyperparameters, this study presents novel method based on graph neural network, named kNNGNN. Given training dat...

Kernel density estimators are the basic tools for density estimation in non-parametric statistics.  The k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in  which  the  bandwidth  is varied depending on the location of the sample points. In this paper‎, we  initially introduce the k-nearest neighbor kernel density estimator in the random left-truncatio...

We study continuous K nearest neighbor queries over moving objects. LUCK stands for Lazy Update algorithm for processing Continuous K-nearest neighbor queries.

K-nearest neighbor (kNN) search is an important problem in data mining and knowledge discovery. Inspired by the huge success of tree-based methodology ensemble methods over last decades, we propose a new method for kNN search, random projection forests (rpForests). rpForests finds nearest neighbors combining multiple kNN-sensitive trees with each constructed recursively through series projectio...