Data warehousing components

Brain stroming group activity

Association

Given the following transactional database Tran ID Items Purchased I1 Strawberry, Litchi, Orange, I2 Strawberry , Butter fruit I3 Butter fruit, Vanilla I4 Strawberry, Litchi, Orange, I5 Banana, Orange I6 Banana I7 Banana, Butter fruit I8 Strawberry, Litchi, Orange, apple I9 Apple, Vanilla I10 Strawberry, Litchi (i) We want to mine all the frequent itemsets in the data using the Apriori & FP growth algorithm. Assume the minimum support level is 30%. (ii) Find all the association rules. The minimum confidence is 70%.

Association

Find all frequent item sets for the given training set using Apriori and FP-growth, respectively and minimum support count is 3. Compare the efficiency of the two mining process. Tran ID Items Purchased I1 {M,O,N,K,E,Y} I2 {D,O,N,K,E,Y} I3 {M,A,K,E} I4 {M,U,C,K,Y} I5 {C,O,O,K,I,E}

Classification

Consider the following training dataset and the original decision tree induction algorithm (ID3). Risk is the class label attribute. The Height values have been already discretized into disjoint ranges. Calculate the information gain if Gender is chosen as the test attribute. Calculate the information gain if Height is chosen as the test attribute. Draw the final decision tree (without any pruning) for the training dataset. Generate all the “IF-THEN rules from the decision tree. Gender Height Risk F (1.5, 1.6) Low M (1.9, 2.0) High F (1.8, 1.9) Medium F (1.8, 1.9) Medium F (1.6, 1.7) Low M (1.8, 1.9) Medium F (1.5, 1.6) Low M (1.6, 1.7) Low M (2.0, 8) High M (2.0, 8) High F (1.7, 1.8) Medium M (1.9, 2.0) Medium F (1.8, 1.9) Medium F (1.7, 1.8) Medium F (1.7, 1.8) Medium