4. (28 points) Giving the following transaction database, we will focus on frequent pattern mining with minimum absolute support of 3, i.e., minsup = 3. TID 1% T1 A,B,C T2 A,D,E T3 B,D T4 A,B,D T5 A,C T6 B,C T7 A,C T8 A,B,C,D,E T9 B,C T10 A,D T11 A,B,C Table 1: Transaction Database. (a) (4 points) For an association rule A => B(s, c), calculate its support 3 and condence 0. (b) (8 points) Find all frequent itemsets using Apriori algorithm. Please use the alphabet- ical ordering A,B, C',D,E for the candidate generation (self-join and pruning) phase. Please show all intermediate steps (like how to scan and lter the data, how to generate candidates with the ordering you are referring to, and if any pruning tricks are available, until you reach your nal result) to get full credit. (c) (10 points) What is the FP-tree corresponding to transactions in Table 1? Requirements: Please insert transactions in the order of T1,T2, . . . ,Tu. You need to draw three FPTrees after inserting T1, T5, and T11 to get full credit. ((1) (6 points) Find all frequent itemsets using the FPGrowth algorithm. Requirements: Please draw a table for the conditional database derived from the FP-tree in the previous question (e.g., see Page 51 of Slides 05) to get full credit