1. Research Project Mining Negative Rules in Large Databases using GRD

2. Rule Discovery Paradigms Classification Rule Discovery Classification Rule Discovery Association Rule Discovery Association Rule Discovery Generalized Rule Discovery Generalized Rule Discovery Rule: A => B Rule: A => B –A is the antecedent –B is the consequent

3. Classification Rule Discovery Aim: Make predictions from rules discovered in data Aim: Make predictions from rules discovered in data Discover a small number of rules that cover most of the training data Discover a small number of rules that cover most of the training data Focus on a single consequent Focus on a single consequent

4. Association Rule Discovery Aim: Searches database to find strong associations between itemsets Aim: Searches database to find strong associations between itemsets Itemsets are subsets of the dataset Itemsets are subsets of the dataset Coverset: set of transactions that an itemset (A) occurs in Coverset: set of transactions that an itemset (A) occurs in

5. Association Rule Discovery (Contd.) Support of A => B: coverset (A U B) / |D| Support of A => B: coverset (A U B) / |D| Confidence of A => B : coverset (A U B) / coverset (A) Confidence of A => B : coverset (A U B) / coverset (A) Min. constraints are defined to accept a rule. Min. constraints are defined to accept a rule. –Minimum support (frequent itemsets) –Minimum confidence (interest)

6. Generalized Rule Discovery Uses the concepts of Association Rule Discovery Uses the concepts of Association Rule Discovery Uses the search method from Classification Rule Discovery – The OPUS Algorithm for an unordered Search. Uses the search method from Classification Rule Discovery – The OPUS Algorithm for an unordered Search. User specifies alternate minimum constraints User specifies alternate minimum constraints

7. Aims  Find negative correlations between Itemsets in a database.  This will be achieved by extending the GRD technique Rule: A => ~B, ~A => B, ~A => ~B Rule: A => ~B, ~A => B, ~A => ~B

8. AIMS (Contd.)  tidsets A = diffset ~A With very little additional computational overheads the negative associations can be calculated With very little additional computational overheads the negative associations can be calculated Assess whether the results of negative correlations are potentially interesting or not Assess whether the results of negative correlations are potentially interesting or not