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Books > Computing & IT > Applications of computing > Artificial intelligence
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Knowledge Discovery and Measures of Interest (Paperback, Softcover reprint of hardcover 1st ed. 2001)
Loot Price: R1,430
Discovery Miles 14 300
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Knowledge Discovery and Measures of Interest (Paperback, Softcover reprint of hardcover 1st ed. 2001)
Series: The Springer International Series in Engineering and Computer Science, 638
Expected to ship within 18 - 22 working days
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Knowledge Discovery and Measures of Interest is a reference book
for knowledge discovery researchers, practitioners, and students.
The knowledge discovery researcher will find that the material
provides a theoretical foundation for measures of interest in data
mining applications where diversity measures are used to rank
summaries generated from databases. The knowledge discovery
practitioner will find solid empirical evidence on which to base
decisions regarding the choice of measures in data mining
applications. The knowledge discovery student in a senior
undergraduate or graduate course in databases and data mining will
find the book is a good introduction to the concepts and techniques
of measures of interest. In Knowledge Discovery and Measures of
Interest, we study two closely related steps in any knowledge
discovery system: the generation of discovered knowledge; and the
interpretation and evaluation of discovered knowledge. In the
generation step, we study data summarization, where a single
dataset can be generalized in many different ways and to many
different levels of granularity according to domain generalization
graphs. In the interpretation and evaluation step, we study
diversity measures as heuristics for ranking the interestingness of
the summaries generated. The objective of this work is to introduce
and evaluate a technique for ranking the interestingness of
discovered patterns in data. It consists of four primary goals: To
introduce domain generalization graphs for describing and guiding
the generation of summaries from databases. To introduce and
evaluate serial and parallel algorithms that traverse the domain
generalization space described by the domain generalization graphs.
To introduce and evaluate diversity measures as heuristic measures
of interestingness for ranking summaries generated from databases.
To develop the preliminary foundation for a theory of
interestingness within the context of ranking summaries generated
from databases. Knowledge Discovery and Measures of Interest is
suitable as a secondary text in a graduate level course and as a
reference for researchers and practitioners in industry.
General
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