Relational data mining (original) (raw)
Relational data mining is the data mining technique for relationaldatabases. Unlike traditional data mining algorithms, which look forpatterns in a single table, relational data mining algorithms look for patterns among multiple tables. For most types of propositionalpatterns, there are corresponding relational patterns. For example,there are relational classification rules (relational classification), relational regression tree, and relational association rules. There are several approaches to relational data mining:
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| dbo:abstract | Relational data mining is the data mining technique for relationaldatabases. Unlike traditional data mining algorithms, which look forpatterns in a single table, relational data mining algorithms look for patterns among multiple tables. For most types of propositionalpatterns, there are corresponding relational patterns. For example,there are relational classification rules (relational classification), relational regression tree, and relational association rules. There are several approaches to relational data mining: 1. * Inductive Logic Programming (ILP) 2. * Statistical Relational Learning (SRL) 3. * Graph Mining 4. * Propositionalization 5. * Multi-view learning (en) |
| dbo:wikiPageExternalLink | http://www.kiminkii.com/safarii.html http://www-ai.ijs.si/SasoDzeroski/RDMBook/ http://relational.fit.cvut.cz http://www.dataconda.net |
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| rdfs:comment | Relational data mining is the data mining technique for relationaldatabases. Unlike traditional data mining algorithms, which look forpatterns in a single table, relational data mining algorithms look for patterns among multiple tables. For most types of propositionalpatterns, there are corresponding relational patterns. For example,there are relational classification rules (relational classification), relational regression tree, and relational association rules. There are several approaches to relational data mining: (en) |
| rdfs:label | Relational data mining (en) |
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