%0 Journal Article
%T An Experiment of <i>K</i>-Means Initialization Strategies on Handwritten Digits Dataset
%A Boyang Li
%J Intelligent Information Management
%P 43-48
%@ 2160-5920
%D 2018
%I Scientific Research Publishing
%R 10.4236/iim.2018.102003
%X Clustering is an important unsupervised classification method which divides data into different groups based some similarity metrics. <em>K</em>-means becomes an increasing method for clustering and is widely used in different application. Centroid initialization strategy is the key step in <em>K</em>-means clustering. In general, <em>K</em>-means has three efficient initialization strategies to improve its performance <em>i.e.</em>, Random, <em>K</em>-means++ and PCA-based <em>K</em>-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 <em>K</em>-means initialization strategies find out almost identical cluster centroids, and they have almost the same results of clustering, but the PCA-based <em>K</em>-means strategy significantly improves running time, and is faster than the other two strategies.
%K &lt
%K i&gt
%K K&lt
%K /i&gt
%K -means
%K Clustering Performance Evaluation
%K Machine Learning
%K Principal Component Analysis
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=82761