A Fast k-Nearest Neighbor Classifier Using Unsupervised Clustering

In this paper we propose a fast method to classify patterns when using a k-nearest neighbor (kNN) classifier. The kNN classifier is one of the most popular supervised classification strategies. It is easy to implement, and easy to use. However, for large training data sets, the process can be time consuming due to the distance calculation of each test sample to the training samples. Our goal is to provide a generic method to use the same classification strategy, but considerably speed up the distance calculation process. First, the training data is clustered in an unsupervised manner to find the ideal cluster setup to minimize the intra-class dispersion, using the so-called “jump” method. Once the clusters are defined, an iterative method is applied to select some percentage of the data closest to the cluster centers and furthest from the cluster centers, respectively. Beside some interesting property discovered by altering the different selection criteria, we proved the efficiency of the method by reducing by up to 71% the classification speed, while keeping the classification performance in the same range.