Q1. In this Assignment you will explore unsupervised knowledge discovery techniques of association rule mining and sequential patterns mining. In particular you will apply some of the techniques discussed in the lectures by tracing the algorithms using sample data sets. You will also experiment with performing market basket analysis using WEKA on a couple of real-world data sets that have been preprocessed and prepared for the tasks in this assignment 1. Consider the following transaction database. Each row represents a single transaction in which the specified items have been purchased. Transaction ID Items Purchased 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 A,B,C,D B,C,D,E,G A,C,G,H,K B,C,D,E,K D,E,F,H,L A,B,C,D,E,L A,D,E,F,L B,1,K,L C,D,F,L A,B,D,E,K C,D,HIK B,C,E,K B,C,D,F A,B,C,D C,H,1,J A,E,F,HL H,K,L A,B,D,H,K D,E,K B,C,D,E,H a. Applying the Apriori algorithm with minimum support of 30% find all the frequent itemsets in the data set. For each step in the algorithm, give the list of frequent itemsets that satisfy minimum support (i.e., for each iteration i, give the set L, along with the support values for the frequent itemsets). b. From the maximal frequent itemsets in part a, generate all association rules that meet a minimum confidence of 75%. In addition to the confidence also specify the Lift (improvement) values for each of the final set of rule you discovered