A direct method for cluster analysis

S. S. Yau, S. C. Chang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

An algorithm based on a least-mean-square (LMS) criterion is presented. This algorithm partitions a multi-dimensional data set directly into a desired number of clusters. The result is compared favorably to existing methods in both performance and computational efficiency. An efficient method for determining a reasonable set of distributed initial cluster centers based on principal component analysis is also presented. This clustering algorithm is shown to converge to a unique minimum based on the LMS criterion and is demonstrated by digital computer simulation and applied to the analysis of vectorcardiograms.

Original languageEnglish (US)
Pages (from-to)215-224
Number of pages10
JournalPattern Recognition
Volume7
Issue number4
DOIs
StatePublished - Dec 1975
Externally publishedYes

Keywords

  • Cluster analysis algorithm
  • Initial cluster centers
  • Least-mean-square criterion
  • Principal component analysis

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'A direct method for cluster analysis'. Together they form a unique fingerprint.

Cite this