Author(s)

Boyang Li

High School Affiliated to Xi’an Jiaotong University, Xi’an, China.

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.

K-means, Clustering Performance Evaluation, Machine Learning, Principal Component Analysis

Li, B. (2018) An Experiment of K-Means Initialization Strategies on Handwritten Digits Dataset. Intelligent Information Management, 10, 43-48. doi: 10.4236/iim.2018.102003.