Embedded Cluster Modelling-A novel method for analysing embedded data sets

✍ Scribed by Worth, Andrew P. ;Cronin, Mark T.D.

• Publisher: John Wiley and Sons
• Year: 1999
• Tongue: English
• Weight: 92 KB
• Volume: 18
• Category: Article
• ISSN: 1611-020X
• DOI: 10.1002/(sici)1521-3838(199907)18:3<229::aid-qsar229>3.0.co;2-g

✦ Synopsis

Cluster Signi®cance Analysis (CSA) is a method for analysing embedded data sets, i.e. data sets in which the objects (chemicals) are divided into two classes (activeyinactive or toxicynon-toxic) and in which one class of objects (typically, the active or toxic chemicals) is found to cluster along one or more variables (e.g. physicochemical descriptors), forming an embedded cluster' surrounded by the diffuse cluster' of objects in the other class (typically, the inactive or non-toxic chemicals). The aim of CSA is to identify variables along which clustering is statistically signi®cant. Having identi®ed signi®cant variables, the investigator may wish to derive a model for classifying active and inactive chemicals on the basis of these variables. In this paper, a method called `embedded cluster modelling' (ECM) is proposed for the derivation of such