Assume you have 4 documents with the following terms:• D1 = “high”, “school”, “football”, “game”, “high”, “score”• D2 = “computer”, “game”, “high”, “score”• D3 = “game”, “computer”, “graphics”, “frameworks”• D4 = “data”, “world”, “development”If the query Q is composed of terms “high” and “school”, what is the relevance of the query to document D1, using the TF.IDF measure?

A node with high hubness value must necessarily have a low authority value.

Given the following itemsets: {A,B,C} {B,C} {A,B,C,D} {A,C,D} {C,D} {B,C} {B,D} What is the support of {B,C} ?

Which of the following is an advantage of collaborative filtering recommendation systems?

You have the following data points:-1, 4, 5, 8, 10Assuming you have done agglomerative hierarchical clustering with clusters are represented by their centroids (average), and at each step the clusters with the closest centroids were merged. If you cut off the process once two clusters existed, which points belonged to these clusters?

What is the worst case and best case competitive ratio for the greedy bipartite matching algorithm?

The Jaccard similarity is an often used measure for collaborative filtering.

9.4; Combine like terms. ( 5 – 3i ) – ( 2 – 7i )

5.7; Use long division to perform each division. x 2   –   5 x   +   6 x   –   2

8.6; Solve 4 x   –   3   –   3   =   4