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Ding, C. and He, X.F. (2004) K-Means Clustering via Principal Component Analysis. Proceedings of the Twenty-First International Conference on Machine Learning, Banff, Alberta, 4-8 July 2004, 29.
https://doi.org/10.1145/1015330.1015408

has been cited by the following article:

• TITLE: An Experiment of K-Means Initialization Strategies on Handwritten Digits Dataset

  AUTHORS: Boyang Li

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

  JOURNAL NAME: Intelligent Information Management, Vol.10 No.2, February 28, 2018

  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.