Towards optimizing data analysis for multi-dimensional data sets

Abstract

The K-Nearest Neighbors (KNN) algorithm is a simple but powerful technique used for data analysis. It identifies existing samples in a dataset which are similar to a new sample by using a distance metric. The new sample can then be classified via a class majority voting of its most similar samples, i.e. nearest neighbors. The KNN algorithm can be applied in many fields, such as recommender systems where it can be used to group related products or predict user preferences. In many cases, the performance of the KNN algorithm diminishes as the size of the dataset increases because the number of comparisons performed increases exponentially. In this paper, we propose a KNN optimization algorithm which leverages vector space models to enhance the nearest neighbors search for a new sample. It accomplishes this enhancement by restricting the search area, and therefore reducing the number of comparisons necessary. The experimental results demonstrate significant performance improvements without compromising the algorithm’s accuracy.