@inproceedings{1c37fcd6ff254f2889060aab876dece5,
title = "Cluster structure of K-means clustering via principal component analysis",
abstract = "K-means clustering is a popular data clustering algorithm. Principal component analysis (PCA) is a widely used statistical technique for dimension reduction. Here we prove that principal components are the continuous solutions to the discrete cluster membership indicators for K-means clustering, with a clear simplex cluster strcuture. Our results prove that PCA-based dimension reductions are particular- lly effective for for K-means clustering. New lower bounds for K-means objective function are derived, which is the total variance minus the eigenvalues of the data covariance matrix.",
author = "Chris Ding and Xiaofeng He",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2004.; 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004 ; Conference date: 26-05-2004 Through 28-05-2004",
year = "2004",
doi = "10.1007/978-3-540-24775-3\_50",
language = "英语",
isbn = "354022064X",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "414--418",
editor = "Honghua Dai and Ramakrishnan Srikant and Chengqi Zhang",
booktitle = "Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings",
address = "德国",
}