problem 1: A database has five transactions. Let min sup = 60% and min con f = 80%.

TID                 Items bought
T100               {M, O, N, K, E, Y}
T200               {D, O, N, K, E, Y}
T300               {M, A, K, E}
T400               {M, U, C, K, Y}
T500               {C, O, O, K, I, E}

a) Find out all frequent item sets using Apriori and FP-growth, correspondingly. Compare the efficiency of the two mining processes.

b) List out all of the strong association rules (with support s and confidence c) matching the following meta rule, where X is a variable representing customers, and itemi denotes variables representing items (e.g., “A”, “B”, etc.): Vx belongs transaction, buys(X, item1)^buys(X, item2))buys(X, item3) [s, c]

problem 2: prepare algorithm for FP-growth.

problem 3: Describe Apriori Algorithm with illustration.

problem 4: Describe how to mine multi-dimensional association rules from Relational data bases.

problem 5: Given a decision tree, you have the option of:

a) Transforming the decision tree to rules and then pruning the resulting rules, or
b) Pruning the decision tree and then converting the pruned tree to rules. What benefits does (a) have over (b)?

problem 6: Distinguish between classification and prediction.

problem 7: What do you mean by tree pruning? Describe how it works?

problem 8: Describe Back propagation algorithm.