Mining Functional Dependencies from Data

1. Mining Functional Dependencies from Data Hong Yao and Howard J. Hamilton Presented By Stephen Lynn

2. Rule Mining • Algorithmic process that takes data as input and yields rules such as: • Association Rules • Implications • Functional dependencies

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

4. Goals and Objectives Design an efficient rule discovery algorithm for mining functional dependencies from a dataset.

5. Implication • Describes relationship between one specific combination of attribute-value pairs. • Binary Data • Propositional Logic {milk, eggs} → {bread}

6. Functional Dependency • Describe relationship between all possible combinations of attribute-value pairs. • Disjoint attributes • True regardless of how many possible attribute values • antecedent → consequent postcode → areacode

7. Search Space

8. Armstrong’s Axioms

9. Equivalent Attributes

10. Nontrivial Closure

11. Base Algorithm • Generate all possible antecedents then test with possible consequents (1 level at a time)

12. Pruning Rules

13. FD_Mine

14. Experimental Summary • 15 Datasets from UCI Machine Learning Repository (2005)

15. Results

16. Results

17. Runtime

18. Analysis • Strengths • Nicely drawn proofs • Weaknesses • Missing good example • Nice to show results with/without pruning • Future Work • Find multivalued dependencies • Find conditional dependencies • Data cleaning