An Experiment of <i>K</i>-Means Initialization Strategies on Handwritten Digits Dataset

Abstract

Clustering is an important unsupervised classification method which divides data into different groups based some similarity metrics. K -means becomes an increasing method for clustering and is widely used in different application. Centroid initialization strategy is the key step in K -means clustering. In general, K -means has three efficient initialization strategies to improve its performance i.e. , Random, K -means++ and PCA-based K -means. In this paper, we design an experiment to evaluate these three strategies on UCI ML hand-written digits dataset. The experiment result shows that the three K -means initialization strategies find out almost identical cluster centroids, and they have almost the same results of clustering, but the PCA-based K -means strategy significantly improves running time, and is faster than the other two strategies.