Explain inter-cluster and intra-cluster distances

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Reference no: EM133115714

Data Mining for Business Analytics and Cyber Security

Exercise

Question 1: Compare the advantages and disadvantages of (a) K-means and (b) K-medoids for clustering.

Discuss a main challenge common to both the K-means and K-medoids algorithms.

Question 2: Explain inter-cluster and intra-cluster distances and their relationship when used to evaluate clustering results?

Question 3: Suppose that the data mining task is to cluster points (with (x, y) representing location) into three clusters, where the points are:

A1(2, 10), A2(2, 5), A3(8, 4), B1(5, 8), B2(7, 5), B3(6, 4), C1(1, 2), C2(4, 9).

The distance function is Euclidean distance. Suppose initially we assign A1, B1, and C1 as the center of each cluster, respectively. Use the k-means algorithm to show only

(a) the three cluster centers after the first round of execution.
(b) the final three clusters.

 

Reference no: EM133115714

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