A New K Nearest Neighbours Algorithm Using Cell Grids for 3D Scattered Point Cloud

Classifying Medical Literature Using k-Nearest-Neighbours Algorithm

The amount of papers published yearly increases since decades. Libraries need to make these resources accessible and available with classification being an important aspect and part of this process. This paper analyzes prerequisites and possibilities of automatic classification of medical literature. We explain the selection, preprocessing and analysis of data consisting of catalogue datasets f...

Instance and Feature Weighted k-Nearest-Neighbours Algorithm

We present a novel method that aims at providing a more stable selection of feature subsets when variations in the training process occur. This is accomplished by using an instance-weighting process –assigning different importances to instances– as a preprocessing step to a feature weighting method that is independent of the learner, and then making good use of both sets of computed weigths in ...

From Point Cloud to 2D and 3D Grids: A Natural Neighbor Interpolation Algorithm using the GPU

With modern LiDAR technology the amount of topographic data, in the form of massive point clouds, has increased dramatically. One of the most fundamental GIS tasks is to construct a grid digital elevation model (DEM) from these point clouds. We present a simple yet very fast natural neighbor interpolation algorithm for constructing a grid DEM from massive point clouds. We use the graphics proce...

An Efficient Algorithm for Bayesian Nearest Neighbours

K-Nearest Neighbours (k-NN) is a popular classification and regression algorithm, yet one of its main limitations is the difficulty in choosing the number of neighbours. We present a Bayesian algorithm to compute the posterior probability distribution for k given a target point within a dataset, efficiently and without the use of Markov Chain Monte Carlo (MCMC) methods or simulation—alongside a...

Smoothing Using Nearest Neighbours