AutoClass solves the problem of automatic discovery of classes in data (sometimes called clustering or unsupervised learning), as distinct from the generation of class descriptions from labeled examples (called supervised learning). It aims to discover the 'natural' classes in the data. AutoClass is applicable to observations of things that can be described by a set of attributes, without referring to other things. The data values corresponding to each attribute are limited to be either numbers or the elements of a fixed set of symbols. With numeric data, a measurement error must be provided.
DocumentationUser intro included; user reference manual included
released on 1 September 2009
|License||Verified by||Verified on||Notes|
|PublicDomain||Janet Casey||31 January 2001|
Leaders and contributors
|James R. Van Zandt||Maintainer|
Resources and communication
|Required to use||glibc|
This entry (in part or in whole) was last reviewed on 22 December 2016.