Ontology Learning

Mining Functional Dependencies from Data

Hong Yao and Howard J. Hamilton

Presented By Stephen Lynn

Ontology Learning

Rule Mining

Algorithmic process that takes data as input and yields rules such as:

Association Rules ImplicationsFunctional dependencies

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Overview Goals/Objectives Implication/Functional Dependencies Base Algorithm 4 Pruning Rules Evaluation Analysis

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Goals and Objectives

Design an efficient rule discovery algorithm for mining functional dependencies from a dataset.

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Implication Describes relationship between one specific

combination of attribute-value pairs.Binary DataPropositional Logic

{milk, eggs} → {bread}

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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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Search Space

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Armstrong’s Axioms

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Equivalent Attributes

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Nontrivial Closure

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Base Algorithm Generate all possible antecedents then test with

possible consequents (1 level at a time)

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Pruning Rules

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FD_Mine

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Experimental Summary 15 Datasets from UCI Machine Learning Repository

(2005)

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Results

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Results

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Runtime

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Analysis Strengths

Nicely drawn proofs Weaknesses

Missing good exampleNice to show results with/without pruning

Future WorkFind multivalued dependenciesFind conditional dependenciesData cleaning