Cluster analysis is the clustering, or grouping, of large data sets (
e.g.,
chemical and/or pharmacological data sets) on the basis of similarity
criteria for appropriately scaled variables that represent the data of
interest. Similarity criteria (distance based, associative, correlative,
probabilistic) among the several clusters facilitate the recognition of
patterns and reveal otherwise hidden structures.
Source:
PAC, 1997, 69, 1137
(Glossary of terms used in computational drug design (IUPAC Recommendations 1997))
on page 1140
Cite as:
IUPAC. Compendium of Chemical Terminology, 2nd ed. (the "Gold Book"). Compiled by
A. D. McNaught and A. Wilkinson. Blackwell Scientific Publications, Oxford (1997).
XML on-line corrected version: http://goldbook.iupac.org (2006-) created by M. Nic,
J. Jirat, B. Kosata; updates compiled by A. Jenkins. ISBN 0-9678550-9-8.
https://doi.org/10.1351/goldbook.
