Mining Functional Dependencies from Data
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Transcript of Mining Functional Dependencies from Data
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Ontology Learning
Mining Functional Dependencies from Data
Hong Yao and Howard J. Hamilton
Presented By Stephen Lynn
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Ontology Learning
Rule Mining
Algorithmic process that takes data as input and yields rules such as:
Association Rules ImplicationsFunctional dependencies
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Ontology Learning
Overview Goals/Objectives Implication/Functional Dependencies Base Algorithm 4 Pruning Rules Evaluation Analysis
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Ontology Learning
Goals and Objectives
Design an efficient rule discovery algorithm for mining functional dependencies from a dataset.
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Ontology Learning
Implication Describes relationship between one specific
combination of attribute-value pairs.Binary DataPropositional Logic
{milk, eggs} → {bread}
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Ontology Learning
Functional Dependency Describe relationship between all possible
combinations of attribute-value pairs.Disjoint attributesTrue regardless of how many possible attribute valuesantecedent → consequent
postcode → areacode
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Ontology Learning
Search Space
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Ontology Learning
Armstrong’s Axioms
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Ontology Learning
Equivalent Attributes
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Ontology Learning
Nontrivial Closure
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Ontology Learning
Base Algorithm Generate all possible antecedents then test with
possible consequents (1 level at a time)
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Ontology Learning
Pruning Rules
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Ontology Learning
FD_Mine
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Ontology Learning
Experimental Summary 15 Datasets from UCI Machine Learning Repository
(2005)
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Ontology Learning
Results
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Ontology Learning
Results
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Ontology Learning
Runtime
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Ontology Learning
Analysis Strengths
Nicely drawn proofs Weaknesses
Missing good exampleNice to show results with/without pruning
Future WorkFind multivalued dependenciesFind conditional dependenciesData cleaning