What distinguishes hierarchical agglomerative clustering from k-means clustering?

What is the Jaccard’s distance between observation C and D?

First, your colleagues come back to you explaining that they run the Apriori algorithm setting the support threshold at 0.5 and the confidence threshold at 0.7. The output showed that 0 rules were generated. Therefore, they conclude there is no recommendation that can be made for viewers of the movie Terminator 3: Rise of the Machines. Do you agree with your colleague?  If not what steps could you take to improve the algorithm so that it generates rules?

Given the following points in a 2D space:   Point X Y A 2 3 B 5 6 C 3 3 D 8 8   Using k-Means clustering where k = 2, two clusters will be created (C1 and C2).  Use this table to answer the next 2 questions.

Why are bedtime routines important for infants?

Assume you run hierarchical clustering on a dataset of 15 observations and obtain the following dendrogram. First, analyze the dendrogram above and identify the best 4-clusters solution. Clearly list below which points belong to each cluster (you can call the clusters C1, C2, C3 and C4).   C1 =  C2 = C3 = C4 =

In hierarchical clustering, the dendrogram can be used to determine the optimal number of clusters by cutting it at different levels.

In K-means clustering, the algorithm stops when centroids no longer change significantly.

Using the points from clusters C1 and C2 found in Question 19, What will be the coordinates of the the 2 centroids (C1 and C2 centroids) after being recalculated after the first iteration?   Please show your work. C1 centroid =  C2 centroid = 

The Apriori algorithm stops generating itemsets when no more frequent itemsets can be found that meet the minimum support threshold.