https://wiki.sarg.dev/index.php?action=history&feed=atom&title=Relational_data_miningRelational data mining - Revision history2026-06-26T00:25:37ZRevision history for this page on the wikiMediaWiki 1.44.2https://wiki.sarg.dev/index.php?title=Relational_data_mining&diff=551788&oldid=previmported>A.conway20 at 11:01, 21 August 20252025-08-21T11:01:13Z<p></p>
<p><b>New page</b></p><div>'''Relational data mining''' is the [[data mining]] technique for relational<br />
databases.<ref>Dzeroski, Saso, [[Nada Lavrač|Lavrač, Nada]] (Eds.), Relational Data Mining, Springer 2001 [https://www.springer.com/computer/database+management+%26+information+retrieval/book/978-3-540-42289-1]</ref> Unlike traditional data mining algorithms, which look for<br />
patterns in a single table ([[propositional patterns]]), <br />
relational data mining algorithms look for patterns among multiple tables<br />
([[relational patterns]]). For most types of propositional<br />
patterns, there are corresponding relational patterns. For example,<br />
there are relational [[classification rule]]s ('''relational classification'''), relational [[Decision tree learning|regression tree]], and relational [[association rule]]s<!-- minor weasel words follow but left as a prompt in case some one can add actual information:, and so on-->.<br />
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There are several approaches to relational data mining:<br />
# [[Inductive_logic_programming|Inductive Logic Programming]] (ILP)<br />
# [[Statistical_relational_learning|Statistical Relational Learning]] (SRL)<br />
# [[Structure mining|Graph Mining]]<br />
# Propositionalization<br />
# Multi-view learning<br />
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==Algorithms==<br />
'''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.<ref name="MRAR: Mining Multi-Relation Association Rules">Ramezani, Reza, Mohamad Saraee, and Mohammad Ali Nematbakhsh; ''[https://salford-repository.worktribe.com/output/1391135/mrar-mining-multi-relation-association-rules MRAR: Mining Multi-Relation Association Rules]'', Journal of Computing and Security, 1, no. 2 (2014)</ref><br />
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==Software==<br />
* [http://www.kiminkii.com/safarii.html Safarii]: a Data Mining environment for analysing large relational databases based on a multi-relational data mining engine.<br />
* [http://www.dataconda.net Dataconda]: a software, free for research and teaching purposes, that helps mining relational databases without the use of SQL.<br />
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==Datasets==<br />
* [http://relational.fit.cvut.cz Relational dataset repository]: a collection of publicly available relational datasets.<br />
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==See also==<br />
*[[Data mining]]<br />
*[[Structure mining]]<br />
*[[Database mining]]<br />
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==References==<br />
{{Reflist}}<br />
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== External links ==<br />
* [http://www-ai.ijs.si/SasoDzeroski/RDMBook/ Web page for a text book on relational data mining]<br />
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[[Category:Machine learning]]<br />
[[Category:Data processing]]<br />
[[Category:Relational model]]<br />
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