文章基本信息

标题：An Experiment of K-Means Initialization Strategies on Handwritten Digits Dataset
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作者：Boyang Li
期刊名称：Intelligent Information Management
印刷版ISSN：2150-8194
电子版ISSN：2150-8208
出版年度：2018
卷号：10
期号：02
页码：43-48
DOI：10.4236/iim.2018.102003
语种：English
出版社：Scientific Research Publishing
摘要：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.
关键词：K-means;Clustering Performance Evaluation;Machine Learning;Principal Component Analysis