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Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 638)
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About this book
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.
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Table of contents (7 chapters)
Authors and Affiliations
Bibliographic Information
Book Title: Knowledge Discovery and Measures of Interest
Authors: Robert J. Hilderman, Howard J. Hamilton
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/978-1-4757-3283-2
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 2001
Hardcover ISBN: 978-0-7923-7507-4Published: 30 September 2001
Softcover ISBN: 978-1-4419-4913-4Published: 08 December 2010
eBook ISBN: 978-1-4757-3283-2Published: 14 March 2013
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: XVIII, 162
Topics: Data Structures and Information Theory, Artificial Intelligence, Discrete Mathematics in Computer Science, Theory of Computation