1. A Microeconomic View of Data Mining Author:Jon et al. Advisor:Dr. Hsu Graduate:ZenJohn Huang IDSL seminar 2001/12/4

2. Outline Motivation Objective Three examples Market segmentation Data mining as sensitivity analysis Segmentation in a model of competition Conclusions Personal opinion

3. Motivation Data mining is about extracting interesting patterns from raw data, but only disjointed discussion of what “ interesting ” means. Patterns are often deemed “ interesting ” on the basis of their confidence and support.

4. Objective Presenting a rigorous framework Based on optimization For evaluating data mining operations Utility in decision-making Studying certain aspects of data mining Economically motivated optimization problems With a large volume of unaggregated data

5. Microeconomic framework Optimization problem Introduction (1/6) D is the domain of all possible decisions f(x) is the utility or value of decision x

6. Mathematical programming and microeconomics Lagrange multipliers and penalty functions[Avriel, 1976] This paper Feasible region D is basically endogenous Objective function f(x) Introduction (2/6)

7. Introduction (3/6) C is a set of agents or other factors influencing the utility of the enterprise Concrete level Abstract level

8. Introduction (4/6) Y i denote the data we have on customer i g(x, y i ) is some fixed function of the decision and the data

9. Introduction (5/6) Aggregation The computational requirements otherwise would be enormous It is difficult to obtain the data y i

10. Introduction (6/6) Fundamental issues Optimization Linear programming Game theory

11. Three Examples (1/3) Beer and diapers Retailer stocks two products in quantities x1, x2; X1+x2 <= c The profit margins in the two products are m1, m2 Part All-or nothing

12. Three Examples (2/3) Market segmentation Residence Business customers

13. Three Examples (3/3) Beer and diapers, revisited Transaction(location, dd, mm, yy, item1, item2, …, itemn) Transaction[location= ‘ Palo alto ’ ] Transaction[location= ‘ Palo alto ’ and 12<tt] Transaction[location= ‘ Palo alto ’ and day- of-the-week(dd,mm,yy)= ‘ Monday ’ ]

14. To segment customers into k clusters Different marketing strategy Different advertising campaign Market Segmentation   

15. Specific Problems N vectors in c 1,…,c n {-1, 1} d K is an integer Find a set of k vectors x 1,…,x k {-1, 1} d Maximize the sum

16. Complexity 1.The segmentation problems corresponding to the following feasible sets D is NP-complete 2.Segmentation problems in the previous theorem can be solved in linear time when the number of dimensions

17. Complexity(cont’d) Theorem 1. The d-dimensional unit ball, even with k=2 2. The d-dimensional unit L 1 ball 3. The r-slice of the d-dimensional hypercube 4. The d-dimensional hypercube, even with k=2 5. The set of all spanning trees of a graph G, even with k=2

18. Complexity(cont’d) Sketch 1. Can be solved by aligning the solution with the cost vector 2. Has only 2d vertices 3. Can be solved by choosing the r most popular elements 4. By simply picking the vertex that coordinate- wise agrees in sign with the cost vector

19. Data Mining As Sensitivity Analysis(1/3)

20. Data Mining As Sensitivity Analysis(2/3) Y i is the table capture from c i

21. Data Mining As Sensitivity Analysis(3/3)

22. Segmentation in a Model of Competition Two-player games Probability distribution

23. Conclusions Presenting a rigorous framework for the automatic evaluation of data mining operations Data mining as an activity by a revenue-maximizing enterprise

24. Personal Opinion Using independent decisions to K mean