Novel KNN Algorithms for Spherical Regions Based on Clustering and Region Division

Abstract

The KNN classification algorithm is one of the most commonly used algorithm in the AI field. But classical KNN classification algorithm does not preprocess data before classification calculation, which results in a long time required for classification and a decrease in classification accuracy. To solve the above problems, this paper proposes two improved algorithms, namely KNNTS, and KNNTS-PK+. The two improved algorithms are based on KNNPK+ algorithm, which uses PK-Means + + algorithm to select the center of the spherical region, and sets the radius of the region to form a sphere to divide the data set in the space. The KNNPK+ algorithm improves the classification accuracy on the premise of stabilizing the classification efficiency of KNN classification algorithm. In order to improve the classification efficiency of KNN algorithm on the premise that the accuracy of KNN classification algorithm remains unchanged, KNNTS algorithm is proposed. It uses tabu search algorithm to select the radius of spherical region, and uses spherical region division method with equal radius to divide the data set in space. On the basis of the first two improved algorithms, KNNTS-PK+ algorithm combines them to divide the data sets in space. After preprocessing the data by two methods, experiments are carried out on the new data set and the classification results were obtained. Results revealed show that the two improved algorithms can effectively improve the classification accuracy and efficiency after the data samples are cut reasonably.