CS590D: Data Mining Prof. Chris Clifton April 21, 2005 Multi-Relational Data Mining.

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Presentation transcript:

CS590D: Data Mining Prof. Chris Clifton April 21, 2005 Multi-Relational Data Mining

What is MRDM? Problem: Data in multiple tables –Want rules/patterns/etc. across tables Solution: Represent as single table –Join the data –Construct a single view –Use standard data mining techniques Example: “Customer” and “Married-to” –Easy single-table representation Bad Example: Ancestor of

Relational Data Network

Basis of Solutions: Inductive Logic Programming ILP Rule: –customer(CID,Name,Age,yes)  Age > 30  purchase(CID,PID,D,Value,PM)  PM = credit card  Value > 100 Learning methods: –Database represented as clauses (rules) –Unification: Given rule (function/clause), discover values for which it holds

Example How do we learn the “daughter” relationship? –Is this classification? Association? Covering Algorithm: “guess” at rule explaining only positive examples –Remove positive examples explained by rule –Iterate

How to make a good “guess” Clause subsumption: Generalize –More general clause (daughter(mary,Y) subsumes daughter(mary,ann) Start with general hypotheses and move to more specific

Issues Search space – efficiency Noisy data –positive examples labeled as negative –Missing data (e.g., a daughter with no parents in the database) What else might we want to learn?

WARMR: Multi-relational association rules

Multi-Relational Decision Trees