--- title: What Is Clustering in Machine Learning? description: Discover clustering in machine learning with K-means, learn about silhouette scores, and visualize data patterns to solve business problems effectively. --- [Learn Computing from the Experts | The Rheinwerk Computing Blog](https://blog.rheinwerk-computing.com) # [What Is Clustering in Machine Learning?](https://blog.rheinwerk-computing.com/what-is-clustering-in-machine-learning) Written by [Rheinwerk Computing](https://blog.rheinwerk-computing.com/author/rheinwerk-computing) | Aug 19, 2026, 1:00:03 PM Clustering groups data points that resemble each other, without anyone telling the algorithm what those groups should be.   People generally find clustering to be intuitive because it’s closely aligned to how we think as humans. We’re trying to simplify the world by identifying patterns and creating frameworks to group what we’re observing. Let’s start our clustering example by running the code below.   `#import required libraries` `import numpy as np` `from sklearn.model_selection import train_test_split` `from sklearn.cluster import KMeans` `from sklearn.datasets import make_blobs` `import matplotlib.pyplot as plt` `from sklearn.metrics import silhouette_score`   `#create sample data` `n_samples = 300` `random_state = 42` `X, y = make_blobs(` `    n_samples=n_samples,` `   random_state=random_state` `   )`   `#execute train test split` `X_train, X_test = train_test_split(` `   X,` `   test_size=0.3,` `   random_state=random_state` `   )`   `#set number of clusters to 3` `n_clusters = 3`   `#create kmeans clustering model` `kmeans = KMeans(` `   n_clusters=n_clusters,` `   random_state=random_state,` `   n_init='auto'` `   )`   `#fit the model to the data` `kmeans.fit(X)`   `#create the predictions on the test data` `test_labels = kmeans.predict(X_test)`   `#create the predctions on the training data` `train_labels = kmeans.predict(X_train)`   `#calculate and print the silhouette score for the training data` `silhouette_avg = silhouette_score(X_train, train_labels)` `print(silhouette_avg)`   `#calculate the silhouette score for the test data` `silhouette_avg = silhouette_score(X_test, test_labels)` `print(silhouette_avg)`   The code is importing new packages for clustering. Kmeans() is the most common approach to clustering. While there are others, our focus will be on the kmeans() algorithm. Let’s discuss this code in more detail:   ## Creating Dataset We’re generating our own data for this example, and sklearn provides us with the make\_blobs() function to do so.   ## Slightly Different Splitting of Data Clustering is *unsupervised learning*, meaning it doesn’t contain a *y* portion to split. This is why, we’re only inputting and outputting the *x* components in our train\_test\_split() function.   ## Clustering Algorithm The clustering algorithm has two new hyperparameters we need to learn. The first is n\_clusters, which says how many groups need to be created from the data. The next is n\_init, which identifies how many different starting points the algorithm will try.   ## Silhouette Metric We have a new metric from the silhouette\_score() function. At a high level, this measures the quality of the clusters by comparing the distance of the data points within a cluster to the data points of the next closest clusters. The score can be anywhere from -1 to 1, with 1 representing very good clusters and -1 representing clusters that may be incorrect. A 0 usually represents clusters that overlap. The output from this code is 0.84 on the training data and 0.86 on the test data, which is pretty good!   Clustering tends to be visual, so we can also visualize our results as shown in the code below with the output shown in the figure. Using the plt.scatter function, we take our centers dataset and provide it with the x and y coordinates (centers\[:,0\] and centers \[:, 1\]) to plot the data.   `#create predictions for full dataset` `all_labels = kmeans.predict(X)`   `#plot the data in a scatterplot` `plt.scatter(X[:, 0], X[:, 1], c=all_labels, s=50, cmap='viridis')`   `centers = kmeans.cluster_centers_` `plt.scatter(centers[:, 0], centers[:, 1], c='red', s=200, alpha=0.75)` `plt.title('K-means Clustering')` `plt.xlabel('Feature 1')` `plt.ylabel('Feature 2')` `plt.show()`     ## Conclusion K-means found three clean groups here, with silhouette scores of 0.84 on training data and 0.86 on test data. Blob data generated by make\_blobs() is designed to separate well, so real datasets will usually score lower and take more experimenting with the n\_clusters value before the groups look right. The process stays the same either way: fit the model, check the silhouette score, plot the output, adjust. Once k-means feels comfortable, hierarchical clustering and DBSCAN are worth exploring for data that forms irregular shapes rather than tidy spheres.   Editor’s note: This post has been adapted from a section of the book *[Applied Machine Learning: Using Machine Learning to Solve Business Problems](https://www.amazon.com/Applied-Machine-Learning-Algorithms-Implementing/dp/1493227580?maas=maas_adg_B9892DE77D408BE8BFBE2E2C759B1EB8_afap_abs&ref_=aa_maas&tag=maas)* by [Jason Hodson](https://www.linkedin.com/in/jason-hodson-21a19ab7/). Jason has worked in data-centric roles for nearly a decade. He currently works as an HR analytics manager, and he has prior experience in a forecasting role using the full range of applied machine learning. In a previous role, Jason wrote the end-to-end code for an enterprise hiring manager and candidate experience process, collaborating with recruiting leaders to understand and leverage data from a company-wide survey. He’s built large data models and dashboards and taught nontechnical users how to adopt and use them. Jason has been a technical mentor in all his roles, helping others develop their analytics and programming skill set. The common thread across Jason’s career is his ability to be a translator for stakeholders, peers, and junior team members. His learning journey also gives him a unique perspective: Before earning a master’s degree in business analytics, he was entirely self-taught. This has made his approach to teaching more practical, allowing concepts to translate better (and faster) into the business world.   This post was originally published 8/2026. 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