Article
Experiments with Domain Knowledge in Unsupervised Learning: U sing and Revising Theories
Fecha
1998-03-09Registro en:
Revista Computación y Sistemas; Vol. 1 No. 3
1405-5546
Autor
Béjar, Javier
Cortés, Ulises
Institución
Resumen
Abstract. Using domain knowledge in unsupervised learning has
shown to be a useful strategy when the set of examples of
a given domain has not an evident structure or presents
some level of noise. This background knowledge can be
expressed as a set of class~fication rules and introduced
as a semantic bias during the learning process.
In this work we present some experiments on the use
of partial domain knowledge with the tool LINNEO+, a
conceptual clustering algorithm. The domain knowledge
(or domain theory) is used to select a set of examples that
will be used to start the learning process, this knowledge
has neither to be complete nor consistent. This bias will
increase the quality oi the final groups and reduce the
effect oi the arder oi the examples. Some measures oi
stability 01 class~fication are used.
The improvement oi the concepts can be used to enhance
and correct the domain knowledge. A set oi heuristics
to revise the original domain theory has been experimented,
yielding to some interesting results.