In MSApriori, what invariant condition guarantees the correctness of the algorithm?

What is the main advantage of using the MSApriori algorithm compared to the standard Apriori?

Assume you have the following data points:1, 3, 4, 7, 10and you execute the k-means algorithm to cluster the data with k=2.If 1 is the first cluster centroid and 4 the second cluster centroid, then what will be the final cluster memberships after the k-means algorithm finishes?

Consider the following scenario: There are three advertisers A, B, and C A bids on query x, B bids on x and y, and C bids on x, y, and z All have budgets of $3. Given the following query stream: x x x y y y z z z What is the sequence of choices, assuming the worst case scenario, for the BALANCE algorithm?

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?