This book presents a study in knowledge discovery in data with
knowledge understood as a set of relations among objects and their
properties. Relations in this case are implicative decision rules
and the paradigm in which they are induced is that of computing
with granules defined by rough inclusions, the latter introduced
and studied within rough mereology, the fuzzified version of
mereology. In this book basic classes of rough inclusions are
defined and based on them methods for inducing granular structures
from data are highlighted. The resulting granular structures are
subjected to classifying algorithms, notably k-nearest neighbors
and bayesian classifiers. Experimental results are given in detail
both in tabular and visualized form for fourteen data sets from UCI
data repository. A striking feature of granular classifiers
obtained by this approach is that preserving the accuracy of them
on original data, they reduce substantially the size of the
granulated data set as well as the set of granular decision rules.
This feature makes the presented approach attractive in cases where
a small number of rules providing a high classification accuracy is
desirable. As basic algorithms used throughout the text are
explained and illustrated with hand examples, the book may also
serve as a textbook.
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