In this exercise, we cluster items in the matrix of Fig. 9.8. Do the following steps.

(a) Cluster the eight items hierarchically into four clusters. The following method should be used to cluster. Replace all 3's, 4's, and 5's by 1 and replace 1's, 2's, and blanks by 0. use the Jaccard distance to measure the distance between the resulting column vectors. For clusters of more than one element, take the distance between clusters to be the minimum distance between pairs of elements, one from each cluster.

(b) Then, construct from the original matrix of Fig. 9.8 a new matrix whose rows correspond to users, as before, and whose columns correspond to clusters. Compute the entry for a user and cluster of items by averaging the nonblank entries for that user and all the items in the cluster.

(c) Compute the cosine distance between each pair of users, according to your matrix from Part (b).