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- W2241103921 abstract "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." @default.
- W2241103921 created "2016-06-24" @default.
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- W2241103921 date "2003-01-01" @default.
- W2241103921 modified "2023-10-16" @default.
- W2241103921 title "Information-Theoretic Measures for Knowledge Discovery and Data Mining" @default.
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- W2241103921 doi "https://doi.org/10.1007/978-3-540-36212-8_6" @default.
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