Description
Question 1. (K-means clustering)
Suppose we have 9 customers and have two attributes (standardized income and standardized
age).
Customer Income Age
A 1 2
B 2 2
C 5 4
D 4 4
E 4 5
F 2 3
G 5 3
H 6 4
I 3 5
Our goal is to group these customers into K=3 groups.
(1) [15pts] Please use K-means algorithm and Euclidean distance to cluster the 9 customers into
3 clusters by hand calculation. The initial centroids MUST be A, D, and H. Please report
the detailed distance matrix and centroids at each step.
(2) [15pts] Please first read and run the supplemented Python codes (K-means-Clusteringdemonstration.ipynb) which demonstrating the example introduced in week-8 lecture. Then,
write your codes in the Jupyter notebook Assignment3-K-means-Clustering.ipynb to solve
this problem. Please only write codes in the fenced parts by # like below:
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