Cart (Loading....) | Create Account
Close category search window

PCA-guided k-Means clustering with incomplete data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Honda, K. ; Dept. of Comput. Sci. & Intell. Syst., Osaka Prefecture Univ., Sakai, Japan ; Nonoguchi, R. ; Notsu, A. ; Ichihashi, H.

This paper considers k-Means clustering of incomplete data sets including missing values. Although the main purpose of k-Means clustering is to partition samples into several homogeneous clusters by minimizing within-cluster errors, it has been shown that a relaxed solution of k-Means can be recovered in a PCA-guided manner. In this paper, the PCA-guided k-Means procedure is extended to a situation in which some observations are missing. Principal component scores, which can be identified with a rotated solution of cluster indicators of k-Means clustering, are estimated in an iterative process without imputation. Besides solving the eigenvalue problem of covariance matrices, k-Means-like partitions are derived through lower rank approximation of the data matrix ignoring missing elements. Several experimental results demonstrate that the PCA-guided process is more robust to initialization problems even though it is based on iterative optimization, just as the k-Means procedure is.

Published in:

Fuzzy Systems (FUZZ), 2011 IEEE International Conference on

Date of Conference:

27-30 June 2011

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.