Statistical Science

Discriminant Analysis and Clustering: Panel on Discriminant Analysis, Classification, and Clustering

Full-text: Open access

Abstract

The general objectives of this report are to provide a summary of the state-of-the-art in discriminant analysis and clustering and to identify key research and unsolved problems that need to be addressed in these two areas. It was prepared under the auspices of the Committee on Applied and Theoretical Statistics of the Board on Mathematical Sciences, National Research Council by its Panel on Discriminant Analysis, Classification, and Clustering. Both methodological and theoretical aspects are reviewed, and a survey of available software and algorithms is provided.

Article information

Source
Statist. Sci., Volume 4, Number 1 (1989), 34-69.

Dates
First available in Project Euclid: 19 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.ss/1177012666

Digital Object Identifier
doi:10.1214/ss/1177012666

Mathematical Reviews number (MathSciNet)
MR997073

Zentralblatt MATH identifier
0955.62588

JSTOR
links.jstor.org

Keywords
Agglomerative methods algorithms classification evolutionary distances high density clusters kernel methods logistic regression pattern recognition minimum spanning tree mixtures nearest neighbor methods single linkage complete linkage ultrametric distances variable selection software displays and diagnostics

Citation

Discriminant Analysis and Clustering: Panel on Discriminant Analysis, Classification, and Clustering. Statist. Sci. 4 (1989), no. 1, 34--69. doi:10.1214/ss/1177012666. https://projecteuclid.org/euclid.ss/1177012666


Export citation