Skip to main content
Log in

Data Model and Classification by Trees: The Minimum Variance Reduction (MVR) Method

  • Published:
Journal of Classification Aims and scope Submit manuscript

O

(n 4), where n is the number of objects. We describe the application of the MVR method to two data models: the weighted least-squares (WLS) model (V is diagonal), where the MVR method can be reduced to an O(n 3) time complexity; a model arising from the study of biological sequences, which involves a complex non-diagonal V matrix that is estimated from the dissimilarity matrix Δ. For both models, we provide simulation results that show a significant error reduction in the reconstruction of T, relative to classical agglomerative algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gascuel, O. Data Model and Classification by Trees: The Minimum Variance Reduction (MVR) Method . J. of Classification 17, 67–99 (2000). https://doi.org/10.1007/s003570000005

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s003570000005

Keywords