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Grouping Similar Data with K-Means Clustering

K-Means Clustering is an unsupervised learning algorithm that automatically groups data into multiple clusters.

K-Means Clustering algorithm divides data into K groups (clusters), with the user specifying the number of clusters (K) in advance.

It is commonly used to analyze customer data to find groups with similar behaviors or to automatically classify news articles by topic.


How is K-Means Clustering Used?​

The goal of K-Means Clustering is to divide the data into groups that are most similar to each other.

To achieve this, the algorithm finds the centroids—central points—to which each data point belongs, and assigns data to these centroids.

For example, if you are categorizing users with similar tastes based on their viewing history in a movie recommendation system, it can be done as follows.

K-Means Clustering Example
User A → Prefers Action Movies → Cluster 1
User B → Prefers Romance Movies → Cluster 2
User C → Prefers Horror Movies → Cluster 3
User D → Prefers Action Movies → Cluster 1

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The chart above can be interpreted as follows:

  • Each ✖ → Individual user's movie preference data

  • Three clusters (Cluster 1, 2, 3) → Groups with similar movie preferences:

    • Red (Cluster 1) → Prefers Action Movies
    • Blue (Cluster 2) → Prefers Horror Movies
    • Green (Cluster 3) → Prefers Romance Movies

  • Yellow X mark → Centroid of each cluster


How K-Means Clustering Works​

K-Means Clustering operates in the following sequence:


1. Select Initial Centroids (K points)​

Randomly initialize K initial centroids.


2. Assign Each Data Point to the Nearest Centroid​

Assign each data point to the cluster of the nearest centroid.

The distance is typically calculated using the Euclidean distance.


3. Recalculate the Centroids​

Compute the average of all data points within each cluster to find new centroids.


4. Repeat Until Convergence​

Repeat these steps until the centroids no longer change, at which point the algorithm terminates.


📌 Uses of K-Means Clustering​

K-Means Clustering can be utilized in various fields as follows:

  • Customer Segmentation: Analyzing purchase patterns to identify groups with similar preferences

  • Image Compression: Grouping similar colors to reduce the number of colors

  • Anomaly Detection: Identifying anomalous data points that belong to different clusters

  • Document Classification: Automatically categorizing news articles or papers by topic


As one of the representative algorithms of unsupervised learning, K-Means Clustering is useful for grouping datasets.

In the next lesson, we will take a short quiz to review what we have learned so far.

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