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Data Analytics (Eng) / Data Analitika (Ing) - 344
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The graph provided below displays the distribution of each of the thirteen features. What three features will influence the distance calculation the most?
The result of a clustering algorithm can be used to perform classification.
Hierarchical clustering is usually defined as an optimization problem
Consider the dendrogram illustrated below. If a horizontal line is drawn at 3.5 how many clusters will be formed?
| 4.0 | x | x | x | x | x | ||||||
| 3.5 | x | x | |||||||||
| 3.0 | x | x | x | x | x | ||||||
| 2.5 | x | x | x | ||||||||
| 2.0 | x | x | x | x | x | x | |||||
| 1.5 | x | x | x | x | x | x | |||||
| 1.0 | x | x | x | x | x | x | x | ||||
| 0.5 | x | x | x | x | x | x | |||||
| 0 | x | x | x | x | x | x | |||||
| A | B | C | D | E | F |
Consider the proximity matrix provided below.
A B C D
A 0 3 2.2 1
B 3 0 1.41 3.16
C 2.2 1.41 0 2
D 1 3.16 2 0
What instances should be joined when average link is used?
Consider the proximity matrix provided below.
A B C D
A 0 3 2.2 1
B 3 0 1.41 3.16
C 2.2 1.41 1 2
D 1 3.16 2 0
Does the proximity matrix contain any errors?
Is the following true for X-means clustering? When a cluster is selected to be split, it is best to select that cluster with the largest intra-cluster distance.
Given the data set D = {(3, 4), (2, 4), (8, 9)}, will k-medoids or k-means clustering produce a centroid that is least sensitive to outliers?
Which of the following best describes the steps involved in the k-means++ initialization process?