Radim Belohlavek
ABSTRACT
We introduce a new type of dependencies for data over domains that are additionally equipped with similarity relations. The dependencies are expressed by if-then rules involving similarities of attribute values. Unlike strict equalities, similarities of attribute values make it possible to provide robust rules and concise descriptions of dependencies regarding attribute values, which are close to how a human expert perceives the data. In the paper, we define the rules, their semantics, entailment, and present an algorithm for computing nonredundant sets of rules, i.e., nonredundant sets of rules describing all if-then dependencies in given data. The algorithm represents basic method for extracting if-then rules from data in similarity-based databases. Due to the limited scope of the paper, all proofs are only skethced or omitted.
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Digital Library
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Index Terms
- Attribute implications in similarity-based databases: semantic entailment and nonredundant bases
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