Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A Review of Classification

A Review of Classification SummaryThe summarization of large quantities of multivariate data by clusters, undefined a priori, is increasingly practiced, often irrelevantly and unjustifiably. This paper attempts to survey the burgeoning bibliography, restricting itself to published, freely available, references of known provenance. A plethora of definitions of similarity and of cluster are presented. The principles, but not details of implementation, of the many empirical classification techniques currently in use are discussed, and limitations and shortcomings in their development and practice are pointed out. Methods based on well-defined mathematical formulations of the problem are emphasized, and other ways of summarizing data are suggested as alternatives to classification. The growing tendency to regard numerical taxonomy as a satisfactory alternative to clear thinking is condemned. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the Royal Statistical Society Series A (Statistics in Society) Oxford University Press

Loading next page...
 
/lp/oxford-university-press/a-review-of-classification-chxsNF6cGb

References (214)

Copyright
© 1971 The Authors
ISSN
0964-1998
eISSN
1467-985X
DOI
10.2307/2344237
Publisher site
See Article on Publisher Site

Abstract

SummaryThe summarization of large quantities of multivariate data by clusters, undefined a priori, is increasingly practiced, often irrelevantly and unjustifiably. This paper attempts to survey the burgeoning bibliography, restricting itself to published, freely available, references of known provenance. A plethora of definitions of similarity and of cluster are presented. The principles, but not details of implementation, of the many empirical classification techniques currently in use are discussed, and limitations and shortcomings in their development and practice are pointed out. Methods based on well-defined mathematical formulations of the problem are emphasized, and other ways of summarizing data are suggested as alternatives to classification. The growing tendency to regard numerical taxonomy as a satisfactory alternative to clear thinking is condemned.

Journal

Journal of the Royal Statistical Society Series A (Statistics in Society)Oxford University Press

Published: Dec 5, 2018

There are no references for this article.