1. Data Mining Tri Nguyen

2. Agenda Data Mining As Part of KDD Decision Tree Association Rules Clustering Amazon Data Mining Examples

3. Putting the results in practical use Data Mining and KDD

4. What is Data Mining? “the automated extraction of hidden predictive information from large databases” Algorithms produce patterns, rules Predict future trends/behavior Used to make business decisions

5. Classification Items belong to classes Given past items’ classification, predict class of new item Example: Issuing credit cards Use information: income, educational background, age, current debts Credit worthiness: Bad, good, excellent

6. Decision Tree Classifiers Internal Node has predicate Leaf node is class To classify instance Start at root node Traverse tree until reach leaf node Each internal node, make decision

7. Credit Risk Decision Tree

8. Decision Tree Construction Some Definitions Purity: > # instances of each leaf belonging to only 1 class means > purity Best Split: split giving the maximum information gain ratio (info gain/info content) Choose attribute and condition resulting in maximum purity

9. Decision Tree Construction

10. Association Rules antecedent  consequent if  then beer  diaper (Walmart) economy bad  higher unemployment Higher unemployment  higher unemployment benefits cost Rules associated with population, support, confidence

11. Association Rules Population: instances such as grocery store purchases Support % of population satisfying antecedent and consequent Confidence % consequent true when antecedent true

12. Association Rules Population MS, MSA, MSB, MA, MB, BA M=Milk, S=Soda, A=Apple, B=beer Support (M  S)= 3/6 (MS,MSA,MSB)/(MS,MSA,MSB,MA,MB, BA) Confidence (M  S) = 3/5 (MS, MSA, MSB) / (MS,MSA,MSB,MA,MB)

13. Clustering “The process of dividing a dataset into mutually exclusive groups such that the members of each group are as "close" as possible to one another, and different groups are as "far" as possible from one another, where distance is measured with respect to all available variables.”

14. Clustering Birch Algorithm points inserted into multidimensional tree items guided to leaf nodes "near" representative internal nodes nearby points clustered into one leaf node

15. Clustering Example of Clustering predict what new movies a person is interested in 1) a person’s past movie preferences 2) others with similar preferences 3) preferences of those in the pool for new movies

16. Clustering 1) cluster people with similar movie preferences 2) given a new movie goer, find a cluster of similar movie goers 3) then predict the cluster's new movie preferences

17. Amazon Examples

21. References http://www.thearling.com/text/dmwhite/dmwhite.htm http://www.cse.ohio-state.edu/~srini/694Z/part1.ppt http://www-aig.jpl.nasa.gov/public/kdd95/tutorials/IJCAI95- tutorial.html http://www-aig.jpl.nasa.gov/public/kdd95/tutorials/IJCAI95- tutorial.html