Basics of k-means clustering

Cluster Analysis in Python

Shaumik Daityari

Business Analyst

Why k-means clustering?

A critical drawback of hierarchical clustering: runtime
K means runs significantly faster on large datasets

Step 1: Generate cluster centers

kmeans(obs, k_or_guess, iter, thresh, check_finite)

obs: standardized observations
k_or_guess: number of clusters
iter: number of iterations (default: 20)
thres: threshold (default: 1e-05)
check_finite: whether to check if observations contain only finite numbers (default: True)

Returns two objects: cluster centers, distortion

How is distortion calculated?

Step 2: Generate cluster labels

vq(obs, code_book, check_finite=True)

obs: standardized observations
code_book: cluster centers
check_finite: whether to check if observations contain only finite numbers (default: True)

Returns two objects: a list of cluster labels, a list of distortions

A note on distortions

kmeans returns a single value of distortions
vq returns a list of distortions.

Running k-means

# Import kmeans and vq functions
from scipy.cluster.vq import kmeans, vq

# Generate cluster centers and labels
cluster_centers, _ = kmeans(df[['scaled_x', 'scaled_y']], 3)
df['cluster_labels'], _ = vq(df[['scaled_x', 'scaled_y']], cluster_centers)

# Plot clusters
sns.scatterplot(x='scaled_x', y='scaled_y', hue='cluster_labels', data=df)
plt.show()

Next up: exercises!

Cluster Analysis in Python