Agrupamento de dados semissupervisionado na geração de regras fuzzy
Abstract
Inductive learning is, traditionally, categorized as supervised and unsupervised.
In supervised learning, the learning method is given a labeled data set (classes
of data are known). Those data sets are adequate for problems of classification
and regression. In unsupervised learning, unlabeled data are analyzed in order to
identify structures embedded in data sets.
Typically, clustering methods do not make use of previous knowledge, such as
classes labels, to execute their job. The characteristics of recently acquired data
sets, great volume and mixed attribute structures, contribute to research on better
solutions for machine learning jobs.
The proposed research fits into this context. It is about semi-supervised fuzzy
clustering applied to the generation of sets of fuzzy rules. Semi-supervised clustering
does its job by embodying some previous knowledge about the data set. The
clustering results are, then, useful for labeling the remaining unlabeled data in the
set. Following that, come to action the supervised learning algorithms aimed at
generating fuzzy rules.
This document contains theoretic concepts, that will help in understanding the
research proposal, and a discussion about the context wherein is the proposal.
Some experiments were set up to show that this may be an interesting solution for
machine learning jobs that have encountered difficulties due to lack of available
information about data.
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