dc.creator | Béjar, Javier | |
dc.creator | Cortés, Ulises | |
dc.date.accessioned | 2013-04-09T16:58:13Z | |
dc.date.available | 2013-04-09T16:58:13Z | |
dc.date.created | 2013-04-09T16:58:13Z | |
dc.date.issued | 1998-03-09 | |
dc.identifier | Revista Computación y Sistemas; Vol. 1 No. 3 | |
dc.identifier | 1405-5546 | |
dc.identifier | http://www.repositoriodigital.ipn.mx/handle/123456789/14947 | |
dc.description.abstract | 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. | |
dc.language | en_US | |
dc.publisher | Revista Computación y Sistemas; Vol. 1 No. 3 | |
dc.relation | Revista Computación y Sistemas;Vol. 1 No. 3 | |
dc.subject | Keywords. Knowledge Acquisition, Domain Theory, Ill-Structured Domains, Clustering Methods. | |
dc.title | Experiments with Domain Knowledge in Unsupervised Learning: U sing and Revising Theories | |
dc.type | Article | |