1. Decision Trees and Association Rules Prof. Sin-Min Lee Department of Computer Science

2. Data Mining: A KDD Process –Data mining: the core of knowledge discovery process. Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation

3. Data Mining process model -DM

4. People and Computers WHAT IS INTELLIGENCE? Model of intelligent behavior Develop theories of human intelligence program computer models test validity of theories New knowledge gained from testing models COGNITIVE SCIENTISTS AI RESEARCHERS

5. Topics in Artificial Intelligence: 1)Machine Vision 2)Robotic 3)Speed Processing 4)Natural Language Processing 5)Theorem Proving 6)General Problem Solving 7)Pattern Recognition 8)Game Playing

6. DEVELOPING AN EXPERT SYSTEM STAGES OF DEVELOPMENT ARE ITERATIVE PROCESSES INDENTIFY PROBLEM INDENTIFY KEY CONCEPTS BOTH GENERAL AND SPECIFIC AND RELATIONSHIPS BETWEEN THEM DETERMIN WHICH TECHNIQUES WILL BE USED KNOWLEDGE REPRESENTATION SEARCH METHODS USE OF DEVELOPMENT TOOLS ADAPT EXISITING EXPERT SYSTEM? IMPLEMENT A PROTOTYPE REFINE PROTOTYPE TESTING DOES EXPERT SYSTEM WORK CORRECTLY

7. COMPONETS OF AN EXPERT SYSTEM USER INTERFACE COMMUNICATION IS BI-DIRECTIONAL IN SIMPLEST FORM DESCRIBE PROBLEM AND SYSTEM RESPONSE WITH ITS RECOMMENDATIONS ADDITIONAL FUNCTION ASK SYSTEM TO EXPLAIN ITS REASONING SYSTEM MAY REQUEST ADDITIONAL INFORMATION CRITICALLY IMPORTANT THAT AN EXPERT SYSTEM IS EASY TO USE NATURAL LANGUAGE FRONT END

8. COMPONENTS OF AN EXPERT SYSTEM INFERENCE ENGINE KNOWLEDGE IS NOT INTERTWINED WITH THE CONTROL STRUCTURE REDUCE EXPERT SYSTEM DEVELOPMENT TIME BY USING AN INFERENCE ENGINE FROM ONE EXPERT SYSTEM WITH A DIFFERENCE KNOWLEDGE BASE EMYCIN (Essential MYCIN) ELIMINATE NEED TO DEVELOP A NEW INFERENCE ENGINE

9. P ~ Q QS~ RQ~ P ~ SR ~ R v S ~ P v S~ R v Q ~ Q v S~ P v Q~ R v P NIL Input clauses Resolvent clauses of depth 1 Resolvent clauses of depth 2 Fig. 39 Process of resolution by breadth-first strategy R ( a ) P ( a, v ) Q ( a, y ) NIL ~S(b)S ( z ) ~ R ( u ) v P ( u, v ) ~P(x,y) v Q(x,y) ~Q(w,x) v S(z) Fig. 40 Resolution process using linear resolution

10. Why Knowledge-based Navigation?

11. Long-term memory Cognitive processor Working Memory Human cognitive architecture shown as compromising three independent elements.

12. Performance Level of Experience Performance Dependent on Experience

13. Performance Level of Experience Performance not Dependent on Experience

14. Modules of an Expert System An expert system can be divided into three modules: 1.A Knowledge Base 2.An Inference Engine 3.A User interface

15. Linda Fleming 1990 COMPONENTS OF AN EXPERT SYSTEM COMPONENTS OF THE KNOWLEDGE BASE DOMAIN OF EXPERT SYSTEM KNOWLEDGE BASE FACTS ABOUT DOMAIN HEURISTICS FOR DOMAIN

16. Linda Fleming 1990 COMPONENTS OF AN EXPERT SYSTEM USER INTERFACE INFERENCE ENGINE RULE INTERPRETER CONTROL STRATEGY KNOWLEDGE BASE RULES or FRAMES or SEMANTIC NETS etc. DATA BASE (WORKING MEMORY) SYSTEM STATUS INITIAL STATES PRESENT STATE FACTS USER

17. Search in State Spaces

22. Decision Trees A decision tree is a special case of a state-space graph. It is a rooted tree in which each internal node corresponds to a decision, with a subtree at these nodes for each possible outcome of the decision. Decision trees can be used to model problems in which a series of decisions leads to a solution. The possible solutions of the problem correspond to the paths from the root to the leaves of the decision tree.

23. Decision Trees Example: The n-queens problem How can we place n queens on an n  n chessboard so that no two queens can capture each other? A queen can move any number of squares horizontally, vertically, and diagonally. Here, the possible target squares of the queen Q are marked with an x.

24. Let us consider the 4-queens problem. Question: How many possible configurations of 4  4 chessboards containing 4 queens are there? Answer: There are 16!/(12!  4!) = (13  14  15  16)/(2  3  4) = 13  7  5  4 = 1820 possible configurations. Shall we simply try them out one by one until we encounter a solution? No, it is generally useful to think about a search problem more carefully and discover constraints on the problem’s solutions. Such constraints can dramatically reduce the size of the relevant state space.

25. Obviously, in any solution of the n-queens problem, there must be exactly one queen in each column of the board. Otherwise, the two queens in the same column could capture each other. Therefore, we can describe the solution of this problem as a sequence of n decisions: Decision 1: Place a queen in the first column. Decision 2: Place a queen in the second column.... Decision n: Place a queen in the n-th column.

26. Backtracking in Decision Trees Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q place 1 st queen place 2 nd queen place 3 rd queen place 4 th queen empty board

29. Neural Network Many inputs and a single output Trained on signal and background sample Well understood and mostly accepted in HEP Decision Tree Many inputs and a single output Trained on signal and background sample Used mostly in life sciences & business

33. Decision tree Basic Algorithm Initialize top node to all examples While impure leaves available –select next impure leave L –find splitting attribute A with maximal information gain –for each value of A add child to L

34. Decision tree Find good split Sufficient statistics to compute info gain: count matrix outlook humidity temperature windy gain: 0.25 bits gain: 0.16 bits gain: 0.03 bits gain: 0.14 bits

35. Decision trees Simple depth-first construction Needs entire data to fit in memory Unsuitable for large data sets Need to “scale up”

36. Decision Trees

37. Planning Tool

38. Decision Trees Enable a business to quantify decision making Useful when the outcomes are uncertain Places a numerical value on likely or potential outcomes Allows comparison of different possible decisions to be made

39. Decision Trees Limitations: –How accurate is the data used in the construction of the tree? –How reliable are the estimates of the probabilities? –Data may be historical – does this data relate to real time? –Necessity of factoring in the qualitative factors – human resources, motivation, reaction, relations with suppliers and other stakeholders

40. Process

41. Advantages

42. Disadvantages

47. Trained Decision Tree (Binned Likelihood Fit) (Limit)

48. Decision Trees from Data Base ExAttAttAttConcept NumSizeColourShapeSatisfied 1medbluebrickyes 2smallredwedgeno 3smallredsphereyes 4largeredwedgeno 5largegreenpillaryes 6largeredpillarno 7largegreensphereyes Choose target : Concept satisfied Use all attributes except Ex Num

49. Rules from Tree IF (SIZE = large AND ((SHAPE = wedge) OR (SHAPE = pillar AND COLOUR = red) ))) OR (SIZE = small AND SHAPE = wedge) THEN NO IF (SIZE = large AND ((SHAPE = pillar) AND COLOUR = green) OR SHAPE = sphere) ) OR (SIZE = small AND SHAPE = sphere) OR (SIZE = medium) THEN YES

52. Association Rule Used to find all rules in a basket data Basket data also called transaction data analyze how items purchased by customers in a shop are related discover all rules that have:- –support greater than minsup specified by user –confidence greater than minconf specified by user Example of transaction data:- – CD player, music’s CD, music’s book – CD player, music’s CD – music’s CD, music’s book – CD player

53. Association Rule Let I = {i 1, i 2, …i m } be a total set of items D a set of transactions d is one transaction consists of a set of items –d  I Association rule:- –X  Y where X  I,Y  I and X  Y =  –support = #of transactions contain X  Y D –confidence = #of transactions contain X  Y #of transactions contain X

54. Association Rule Example of transaction data:- – CD player, music’s CD, music’s book – CD player, music’s CD – music’s CD, music’s book – CD player I = {CD player, music’s CD, music’s book} D = 4 #of transactions contain both CD player, music’s CD =2 #of transactions contain CD player =3 CD player  music’s CD (sup=2/4, conf =2/3 );

56. Association Rule How are association rules mined from large databases ? Two-step process:- –find all frequent itemsets –generate strong association rules from frequent itemsets

57. 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

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

59. 2. Association rules Support Every association rule has a support and a confidence. “The support is the percentage of transactions that demonstrate the rule.” Example: Database with transactions ( customer_# : item_a1, item_a2, … ) 1: 1, 3, 5. 2: 1, 8, 14, 17, 12. 3: 4, 6, 8, 12, 9, 104. 4: 2, 1, 8. support {8,12} = 2 (,or 50% ~ 2 of 4 customers) support {1, 5} = 1 (,or 25% ~ 1 of 4 customers ) support {1} = 3 (,or 75% ~ 3 of 4 customers)

60. 2. Association rules Support An itemset is called frequent if its support is equal or greater than an agreed upon minimal value – the support threshold add to previous example: if threshold 50% then itemsets {8,12} and {1} called frequent

61. 2. Association rules Confidence Every association rule has a support and a confidence. An association rule is of the form: X => Y X => Y: if someone buys X, he also buys Y The confidence is the conditional probability that, given X present in a transition, Y will also be present. Confidence measure, by definition: Confidence(X=>Y) equals support(X,Y) / support(X)

62. 2. Association rules Confidence We should only consider rules derived from itemsets with high support, and that also have high confidence. “A rule with low confidence is not meaningful.” Rules don’t explain anything, they just point out hard facts in data volumes.

63. 3. Example Example: Database with transactions ( customer_# : item_a1, item_a2, … ) 1: 3, 5, 8. 2: 2, 6, 8. 3: 1, 4, 7, 10. 4: 3, 8, 10. 5: 2, 5, 8. 6: 1, 5, 6. 7: 4, 5, 6, 8. 8: 2, 3, 4. 9: 1, 5, 7, 8. 10: 3, 8, 9, 10. Conf ( {5} => {8} ) ? supp({5}) = 5, supp({8}) = 7, supp({5,8}) = 4, then conf( {5} => {8} ) = 4/5 = 0.8 or 80%

64. 3. Example Example: Database with transactions ( customer_# : item_a1, item_a2, … ) 1: 3, 5, 8. 2: 2, 6, 8. 3: 1, 4, 7, 10. 4: 3, 8, 10. 5: 2, 5, 8. 6: 1, 5, 6. 7: 4, 5, 6, 8. 8: 2, 3, 4. 9: 1, 5, 7, 8. 10: 3, 8, 9, 10. Conf ( {5} => {8} ) ? 80% Done. Conf ( {8} => {5} ) ? supp({5}) = 5, supp({8}) = 7, supp({5,8}) = 4, then conf( {8} => {5} ) = 4/7 = 0.57 or 57%

65. 3. Example Example: Database with transactions ( customer_# : item_a1, item_a2, … ) Conf ( {5} => {8} ) ? 80% Done. Conf ( {8} => {5} ) ? 57% Done. Rule ( {5} => {8} ) more meaningful then Rule ( {8} => {5} )

66. 3. Example Example: Database with transactions ( customer_# : item_a1, item_a2, … ) 1: 3, 5, 8. 2: 2, 6, 8. 3: 1, 4, 7, 10. 4: 3, 8, 10. 5: 2, 5, 8. 6: 1, 5, 6. 7: 4, 5, 6, 8. 8: 2, 3, 4. 9: 1, 5, 7, 8. 10: 3, 8, 9, 10. Conf ( {9} => {3} ) ? supp({9}) = 1, supp({3}) = 1, supp({3,9}) = 1, then conf( {9} => {3} ) = 1/1 = 1.0 or 100%. OK?

67. 3. Example Example: Database with transactions ( customer_# : item_a1, item_a2, … ) Conf( {9} => {3} ) = 100%. Done. Notice: High Confidence, Low Support. -> Rule ( {9} => {3} ) not meaningful

68. 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)