Problem 1. K-means Calculations (by hand, calculator) (10 points)

For the medicine data set discussed in class, use K-means with the Manhattan distance metric for clustering analysis by setting K = 2 and initializing seeds as C1 = A and C2 = C. You should show the steps for the calculations made by the K-means algorithm to get full points. Then, answer the three questions below.

1. How many steps were required for convergence?

2. What are the memberships of two clusters after convergence?

3. What are the centroids (coordinates) of two clusters after convergence?

Page Example Problem Suppose we have 4 types of medicines and each has two attributes (pH and weight index). Our goal is to group these objects into K 2 group of medicine. 4 5 Medicine Weight pH-lndex 2 A. B 4 4 attribute 1 (X): weight index