Introduction to Machine Learning: K Means

Welcome to our fourth installment on Machine Learning. In this module we’re going to cover K-Means. K-Means is a clustering algorithm based on the hyperparameter “K” which dictates how many clusters there will be. A hyperparameter is just a parameter that we can adjust. Each cluster has a “centroid” or a central point that will be the anchor of our cluster. Here are the steps to the K-Means algorithm:

  1. Plot the data points
  2. Plot K centroids randomly
  3. Calculate distances from each point to each centroid
  4. Assign a label to each point equal to the centroid it’s clustered to
  5. Calculate the center of each cluster → that becomes the new centroid
  6. Repeat steps 3 to 5 until either:
    1. The centroid stops moving
    2. The data points are not assigned new centroids after an iteration
    3. We reach the maximum number of iterations

Video Tutorial:

Here’s a visualization of K-Means:

We’re not going to manually implement K-Means in this introductory module, we’re just going to use Python’s SKLearn module which already has an implementation for us. Let’s get into it. To start using K-Means we’ll have to install some libraries. We’ll need the sklearn library which has an implementation of K-Means that we can just use, numpy which contains numerical operators, pandas which is the de facto data organization library for Python, and matplotlib which we’ve already used many times and is the best plotting library for Python. We can install these with just one line in the command line:

pip install sklearn numpy pandas matplotlib

Randomly Generated Sample Data K Means

Alright now that we’ve got our libraries installed, we’re ready to go. We’ll cover two different K-Means examples here. Example number 1 is going to be on a contrived example of randomly generated data with two centroids. Example number 2 is going to be on the digits dataset provided by sklearn. As always, the first thing we’ll do is handle our imports.

import random
import pandas as pd
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

Generate Random Sample Data

Next we’re going to randomly generate 100 data points. The points will be two clusters, one around (0,0) and the other around (5,5). We’ll do this with a for loop that loops 100 times and generates a cluster around (0,0) on even iterations and around (5,5) on odd iterations. To randomly generate data points, we’ll create two values, an x and a y value. Each value will be made by generating a uniform random number between -1 and 1 and adding it to either 0 or 5. Once we’ve generated all 100 samples we’ll convert our data into a pandas DataFrame object for further processing.

samples = 100
data = []
for i in range(100):
    if i%2 == 0:
        base = 0
    else:
        base = 5
    x = random.uniform(-1,1) + base
    y = random.uniform(-1,1) + base
    data.append([x,y])
df_rand = pd.DataFrame(data)

Now we can just use sklearn to implement K-Means with 2 clusters. First we’ll create a K-Means object and then call its fit_predict module on the DataFramewe made earlier. Once we have our labels, we’ll separate out the labeled data into two separate dataframes to graph.

k2means = KMeans(n_clusters=2)
label_rand=k2means.fit_predict(df_rand)
flabels1 = df_rand[label_rand==1]
flabels0 = df_rand[label_rand==0]

Plot Randomized Sample Data

Now all we have to do is scatter plot these with matplotlib. We’ll also get the centroids using the cluster_centers_ attribute of the K Means object we created earlier. 

plt.scatter(flabels1[0], flabels1[1], label=0)
plt.scatter(flabels0[0], flabels0[1], label=1)
centroids_rand = k2means.cluster_centers_
plt.scatter(centroids_rand[:,0], centroids_rand[:,1], s=80, color="black")
plt.legend()
plt.xlabel("X")
plt.ylabel("Y")
plt.title("Randomly Generated Two Centroid K Means")
plt.show()

Once we plot these, we should see something like the image below.

Digits Dataset K Means

Alright now that we’ve seen a contrived example, let’s take a look at what a more real-life like example will be. For this example, we’ll be running K Means on the digits dataset. As always, we’ll start off by importing our libraries. We’ll import the load_digits module from sklearn.datasets to load the digits dataset. We’ll import PCA from decomposition to turn this dataset with 64 features into a dataset with 2 features. PCA is Principal Component Analysis, in this example we’ll be using it for dimensionality reduction. We imported KMeans and matplotlib.pyplot already above but I just put them here to show you that we need those libraries for this example. We’ll also need numpy for this example.

from sklearn.datasets import load_digits
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
import numpy as np
import matplotlib.pyplot as plt

We’ll start out by loading up the digits and applying PCA to turn our 64 feature dataset into a 2 feature dataset.

data = load_digits().data
pca = PCA(n_components=2)
df = pca.fit_transform(data)

Load and Examine Data

Once we’ve transformed our data let’s apply KMeans. We’ll give it 10 clusters since there are 10 digits.

kmeans = KMeans(n_clusters=10)
label = kmeans.fit_predict(df)
print(label)

The labels will be a list of numbers where each number is KMeans prediction of each dataset.

This is all there is to KMeans, let’s get into what it looks like when we plot it out. We’ll use np, our alias for the numpy library to create a set of unique labels from the labels we made earlier. Then we’ll use the cluster_centers_ from the K Means object we created to get the centroids. Now, for each of our unique labels, we’ll plot the data points that correspond to that label on a scatter plot. Once we’ve plotted all the labels, we’ll create a scatter plot of the centroids. For the centroids you’ll notice that I passed in an s parameter. This parameter accounts for the size of the point, a regular point is size 72.

Plot Data

Once we’ve plotted our data, we simply label our graph, add a legend, and then print it out.

unique_labels = np.unique(label)
centroids = kmeans.cluster_centers_
for i in unique_labels:
    plt.scatter(df[label==i, 0], df[label==i, 1], label=i)
 
# s is a size indicator
 
plt.scatter(centroids[:,0], centroids[:,1], s=80, color="black")
plt.xlabel("X")
plt.ylabel("Y")
plt.title("Digits K Means")
plt.legend()
plt.show()

Our plot should look something like this:

That’s it, that’s all there is to K-Means. Pretty simple, you can implement it in just a few lines.

Learn More

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Yujian Tang
Yujian Tang

I started my professional software career interning for IBM in high school after winning ACSL two years in a row. I got into AI/ML in college where I published a first author paper to IEEE Big Data. After college I worked on the AutoML infrastructure at Amazon before leaving to work in startups. I believe I create the highest quality software content so that’s what I’m doing now. Drop a comment to let me know!

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