2003 | OriginalPaper | Buchkapitel
Information-Theoretic Measures for Knowledge Discovery and Data Mining
verfasst von : Y. Y. Yao
Erschienen in: Entropy Measures, Maximum Entropy Principle and Emerging Applications
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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A database may be considered as a statistical population, and an attribute as a statistical variable taking values from its domain. One can carry out statistical and information-theoretic analysis on a database. Based on the attribute values, a database can be partitioned into smaller populations. An attribute is deemed important if it partitions the database such that previously unknown regularities and patterns are observable. Many information-theoretic measures have been proposed and applied to quantify the importance of attributes and relationships between attributes in various fields. In the context of knowledge discovery and data mining (KDD), we present a critical review and analysis of information-theoretic measures of attribute importance and attribute association, with emphasis on their interpretations and connections.