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.