Design and Implementation of an Improved K-Means Clustering Algorithm

Abstract

Aiming at the problems of the traditional K-means clustering algorithm, such as the local optimal solution and the slow clustering speed caused by the uncertainty of k value and the randomness of the initial cluster center selection, this paper proposes an improved KMeans clustering method. The algorithm first uses the idea of the elbow rule based on the sum of squares of errors to obtain the appropriate number of clusters k, then uses the variance as a measure of the degree of dispersion of the samples, and selects k data points with the smallest variance and the distance greater than the average distance of the samples as the initial clustering center of the data set. Finally, combined with the “triangular inequality principle,” the unnecessary distance calculation of the KMeans algorithm in the iterative process is reduced, and the algorithm operation efficiency is improved. The results show that the improved KMeans clustering algorithm is tested on the UCI data set. Compared with the traditional k-means algorithm and Canopy-KMeans algorithm, the accuracy and speedup ratio of the improved KMeans clustering algorithm are significantly improved, and the clustering quality is improved.