Intelligent decision support is based on human knowledge related to a
specific part of a real or abstract world. When the knowledge is gained
by experience, it is induced from empirical data. The data structure,
called an information system, is a record of objects described by a set
of attributes.
Knowledge is understood here as an ability to classify objects. Objects
being in the same class are indiscernible by means of attributes and
form elementary building blocks (granules, atoms). In particular, the
granularity of knowledge causes that some notions cannot be expressed
precisely within available knowledge and can be defined only vaguely. In
the rough sets theory created by Z. Pawlak each imprecise concept is
replaced by a pair of precise concepts called its lower and upper
approximation. These approximations are fundamental tools and reasoning
about knowledge.
The rough sets philosophy turned out to be a very effective, new tool
with many successful real-life applications to its credit.
It is worthwhile stressing that no auxiliary assumptions are needed
about data, like probability or membership function values, which is its
great advantage.
The present book reveals a wide spectrum of applications of the rough
set concept, giving the reader the flavor of, and insight into, the
methodology of the newly developed disciplines. Although the book
emphasizes applications, comparison with other related methods and
further developments receive due attention.
