automatic classification or clustering
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.
