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Relational data mining is the data mining technique for relational databases.〔Dzeroski, Saso, Lavrač, Nada (Eds.), Relational Data Mining, Springer 2001 ()〕 Unlike traditional data mining algorithms, which look for patterns in a single table (propositional patterns), relational data mining algorithms look for patterns among multiple tables (relational patterns). For most types of propositional patterns, there are corresponding relational patterns. For example, there are relational classification rules, relational regression tree, relational association rules, and so on. There are several approaches to relational data mining: # Inductive Logic Programming (ILP) # Statistical Relational Learning (SRL) # Graph Mining # Propositionalization # Multi-view learning ==Algorithms== Multi-Relation Association Rules: Multi-Relation Association Rules (MRAR) is a new class of association rules which in contrast to primitive, simple and even multi-relational association rules (that are usually extracted from multi-relational databases), each rule item consists of one entity but several relations. These relations indicate indirect relationship between the entities. Consider the following MRAR where the first item consists of three relations ''live in'', ''nearby'' and ''humid'': “Those who ''live in'' a place which is ''near by'' a city with ''humid'' climate type and also are ''younger'' than 20 -> their ''health condition'' is good”. Such association rules are extractable from RDBMS data or semantic web data.〔Ramezani, Reza, Mohamad Saraee, and Mohammad Ali Nematbakhsh; ''MRAR: Mining Multi-Relation Association Rules'', Journal of Computing and Security, 1, no. 2 (2014)〕 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Relational data mining」の詳細全文を読む スポンサード リンク
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