FAQ: K-Means Clustering - Implementing K-Means: Step 2


#1

This community-built FAQ covers the “Implementing K-Means: Step 2” exercise from the lesson “K-Means Clustering”.

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This exercise can be found in the following Codecademy content:

Data Science

Machine Learning

FAQs on the exercise Implementing K-Means: Step 2

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#2

Distances to each centroid

for i in range(len(samples)):
for j in range(k):
distances[j] = distance(sepal_length_width[i],centroids[j])
cluster = np.argmin(distances)
labels[i] = cluster

Codecademy’s solution should probably include that second for loop for k, since everything else is based on a variable number of centroids.

Also, an alternative solution to using np.zeros is to just do [0] * k, or [0] * len(samples).