This is Q1-c, please see.

c. The following dataset contains 7 transactions of 6 items: apple, bread, carrot, donut, egg, and fish. Calculate (i) support( apple } ), (ii) support( { donut } ), (iii) support( apple }=> donut } ), and (iv) confidence( { apple }=>{ donut } ). (Note that this dataset is also used in some later questions.) [5] - T0: apple, bread, egg, fish - T1: apple, bread, carrot, donut, egg - T2: bread, egg - T3: bread, donut, egg - T4: apple, bread, egg - T5: apple, bread, egg, fish - T6: apple, carrot, donut, egg Question 2 - Unsupervised learning algorithms [25 marks] a. Apply Apriori to the dataset in Q1-c to find frequent itemsets for the minimum support value of 0.4. Show your work of identifying the candidate and frequent 1-itemsets, 2-itemsets, and so on. [12] b. Apply k-means to cluster 6 data points (1,1),(1,2),(1,3),(2,1),(2,2), and (4, 3) to 2 clusters. Use the initial centroids (1,0) and (2,2). Show your work of computing the distances and centroid updates in each iteration of k-means, and present the values in up to 2 decimal places. [13]