Associative graph data structures used for acceleration of K nearest neighbor classifiers

Abstract

This paper introduces a new associative approach for significant acceleration of k Nearest Neighbor classifiers (kNN). The kNN classifier is a lazy method, i.e. it does not create a computational model, so it is inefficient during classification using big training data sets because it requires going through all training patterns when classifying each sample. In this paper, we propose to use Associative Graph Data Structures (AGDS) as an efficient model for storing training patterns and their relations, allowing for fast access to nearest neighbors during classification made by kNNs. Hence, the AGDS significantly accelerates the classification made by kNNs, especially for large and huge training datasets. In this paper, we introduce an Associative Acceleration Algorithm and demonstrate how it works on this associative structure substantially reducing the number of checked patterns and quickly selecting k nearest neighbors for kNNs. The presented approach was compared to classic kNN approaches successfully.