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

• 作者: Boyang Li
• 来源期刊: 智能信息管理(英文)
• 学科: 工学
• 关键词: K-MEANS CLUSTERING Performance Evaluation Machine Learning Principal Component Analysis
• 年，卷（期）: 2018,（2）
• 页码范围: 43-48
• 页数: 6页
• 分类号: TP39

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.