Observational calculi were introduced in the 1960's as a tool of
logic of discovery. Formulas of observational calculi correspond to
assertions on analysed data. Truthfulness of suitable assertions
can lead to acceptance of new scientific hypotheses. The general
goal was to automate the process of discovery of scientific
knowledge using mathematical logic and statistics. The GUHA method
for producing true formulas of observational calculi relevant to
the given problem of scientific discovery was developed.
Theoretically interesting and practically important results on
observational calculi were achieved. Special attention was paid to
formulas - couples of Boolean attributes derived from columns of
the analysed data matrix. Association rules introduced in the
1990's can be seen as a special case of such formulas. New results
on logical calculi and association rules were achieved. They can be
seen as a logic of association rules. This can contribute to
solving contemporary challenging problems of data mining research
and practice. The book covers thoroughly the logic of association
rules and puts it into the context of current research in data
mining. Examples of applications of theoretical results to real
problems are presented. New open problems and challenges are
listed. Overall, the book is a valuable source of information for
researchers as well as for teachers and students interested in data
mining.
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