K Means Clustering Matlab [With Source Code]

K-Means clustering is a powerful tool for diverse applications like image segmentation and market analysis, thanks to its specialized toolboxes and seamless integrations. 

MATLAB’s ability to handle complex data preprocessing, advanced visualization, and algorithm customization (e.g., custom distance metrics or k-means++) makes it ideal for these tasks. 

Its intuitive interface and built-in functions streamline workflows, enabling precise parameter tuning and validation for efficient clustering, even with large datasets.

Enhance your data skills with upGrad’s comprehensive data science courses and learn how to apply K-means clustering in MATLAB to solve complex data problems. Become an expert in analyzing datasets effectively and deriving meaningful insights. Start your learning journey today!

K-Means Clustering MATLAB: Overview and Importance

K-means clustering is a popular unsupervised learning algorithm that groups data into clusters based on similarity, with each data point assigned to the nearest mean. MATLAB streamlines this process using the kmeans() function, offering an efficient and user-friendly platform for both beginners and experts. 

Its robust tools make it ideal for pattern recognition and data simplification across domains like finance, biology, and image processing.

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Here are the major features of this process:

Key Features of K Means Clustering MATLAB 

MATLAB provides a robust environment for clustering tasks, offering several key advantages, including:

• Algorithm Support: MATLAB supports K-Means with advanced options like initialization methods (e.g., random, k-means++), iteration limits, and convergence checks to enhance clustering accuracy.
• Interactive Visualization: Tools like scatter plots and silhouette plots help interpret clustering results, offering insights into cluster distribution and cohesion.
• Integration with Preprocessing Tools: Preprocessing techniques like normalization enhance clustering accuracy.

Example: Before applying K-Means, a dataset with numerical variables can be standardized using MATLAB’s normalize() function to ensure uniform scaling.

Now that the main features of K Means Clustering MATLAB have been covered, let us have a look at what makes MATLAB highly suitable for K-Means clustering:

Why MATLAB Excels in K-Means Clustering?

MATLAB provides a seamless workflow for clustering tasks, integrating preprocessing, clustering, and evaluation in one platform. Its built-in kmeans MATLAB functions allow easy implementation without extensive coding.  Some examples that showcase its utility include:

Examples of K-Means Clustering in MATLAB

• Image Segmentation:

Cluster RGB values of an image to divide it into regions based on color similarity. This technique is widely used in practical scenarios like isolating tumors in medical imaging or identifying objects in satellite images for environmental analysis.

L = imsegkmeans(image, 5); % Segments the image into 5 clusters
imshow(label2rgb(L));

• Market Analysis:

Cluster customers by age, income, and spending to identify behavioral patterns. This helps in creating personalized marketing strategies.
[idx, C] = kmeans(customerData, 3); % Groups data into 3 segments
scatter3(customerData(:,1), customerData(:,2), customerData(:,3), 10, idx);

Also Read: Clustering in Machine Learning: Learn About Different Techniques and Applications

Having understood the significance and basic concepts of K-means clustering in MATLAB, the next step is to learn how to implement it effectively. Let’s dive into the practical aspects of coding and executing K-means clustering to bring its capabilities to life in MATLAB.

How Can You Implement K-Means Clustering MATLAB?

K-means clustering is a powerful technique for grouping data points into clusters based on their similarities. MATLAB simplifies the implementation of this algorithm through its built-in kmeans() function, which enables efficient clustering with minimal code. 

This section provides a detailed explanation of K-means clustering in MATLAB, starting with the syntax, progressing through the steps to implement it, and concluding with result interpretation.

MATLAB Syntax for K-Means Clustering

Before diving into implementation, it's essential to understand the syntax of MATLAB's kmeans() function.

[clusterIdx, clusterCenters] = kmeans(data, k, Name, Value);

• Purpose: The kmeans() function partitions data points into k clusters using an iterative algorithm to minimize the distance between points and their assigned cluster centroids.
• Parameters:
  • data: A matrix of data points where rows represent individual points and columns represent features.
  • k: The number of clusters to create.
  • clusterIdx: The output array assigning each data point to a cluster.
  • clusterCenters: The coordinates of the centroids of each cluster.
• Optional Name-Value Pairs:
  • 'Distance': Specifies the distance metric (e.g., 'sqeuclidean', 'cityblock').
  • 'Replicates': The number of times to repeat the clustering with different initial centroid positions.
  • 'MaxIter': The maximum number of iterations allowed for the algorithm.

Quick Tip: Always preprocess your data by scaling features to avoid bias caused by differing feature scales. For instance, use MATLAB's normalize() function to standardize your data before clustering:

data = normalize(data);

Understanding the syntax ensures you can tailor the function to specific data and clustering needs. With this foundation, let’s explore how K-means clustering is implemented in MATLAB:

Steps:

Here are the major steps involved in the implementation of K-Means clustering in MATLAB in detail:  

Step 1: Data Preparation in MATLAB

Data should be in matrix format, with rows as observations and columns as features. In MATLAB, you can load data from external sources or generate synthetic data to prepare it for clustering.

Example: Generating synthetic data for clustering.

% Generate random data with three clusters
rng(1); % Set seed for reproducibility
data = [randn(50,2)*0.75 + ones(50,2); 
       randn(50,2)*0.5 - ones(50,2); 
       randn(50,2)*0.6 + [2, -2]]; % Create three clusters

• Explanation:
  • The randn() function generates random data points.
  • Adjustments to the scale and position of data points create distinct clusters.
  • Using rng() ensures reproducibility of results.

With the data prepared, the next step is applying the clustering algorithm.

Step 2: Applying the kmeans() Function

The kmeans() function is the core of the clustering process. It assigns each data point to one of the specified clusters and calculates the cluster centroids iteratively.

Example: Applying the kmeans() function to the prepared data.

% Number of clusters
k = 3; 
% Perform K-means clustering
[clusterIdx, clusterCenters] = kmeans(data, k, 'Distance', 'sqeuclidean', 'Replicates', 5);

• Explanation:
  • k = 3: Specifies the desired number of clusters.
  • 'Distance', 'sqeuclidean': Uses squared Euclidean distance for clustering.
  • 'Replicates', 5: Repeats clustering 5 times to reduce the likelihood of poor initialization.

The output includes:

• clusterIdx: Cluster assignments for each data point.
• clusterCenters: Coordinates the centroids for each cluster.

Now that clustering is complete, the next step involves visualizing and interpreting the results.

Step 3: Visualizing the Results

Visualization is crucial for understanding the clustering process and verifying the quality of clusters. MATLAB offers plotting tools to display the clustered data and centroids.

Example: Scatter plot visualization of clusters.

% Plot data points with clusters
figure;
gscatter(data(:,1), data(:,2), clusterIdx, 'rgb', 'o', 8);
hold on;
% Plot centroids
plot(clusterCenters(:,1), clusterCenters(:,2), 'kx', 'MarkerSize', 15, 'LineWidth', 3);
title('K-Means Clustering Results');
xlabel('Feature 1');
ylabel('Feature 2');
legend('Cluster 1', 'Cluster 2', 'Cluster 3', 'Centroids');

• Explanation:
  • gscatter(): Creates a scatter plot with different colors for each cluster.
  • Centroids are marked with a distinct shape (kx) to differentiate them from data points.

The plot reveals the distribution of clusters and the central positions of the cluster centroids, providing visual confirmation of the clustering process.

Step 4: Interpreting the Results

Once the clusters are visualized, it's essential to interpret the output for meaningful insights.

• Cluster Labels (clusterIdx):

Each data point is assigned a cluster label (e.g., Cluster 1, 2, or 3), indicating its group.

• Centroid Positions (clusterCenters):

The centroid coordinates represent the mean values of the points within each cluster, summarizing the cluster’s characteristics.

• Validation of Results:

Check for well-separated clusters in the plot.

Analyze cluster sizes and centroid positions to ensure they align with expectations.

Complete MATLAB Code Example

% Step 1: Data Preparation
rng(1);
data = [randn(50,2)+2; randn(50,2)-2; randn(50,2)+[-2, 2]];
% Step 2: K-Means Clustering
k = 3;
[idx, C] = kmeans(data, k, 'Distance', 'sqeuclidean', 'Replicates', 5);
% Step 3: Visualization
gscatter(data(:,1), data(:,2), idx, 'rbg', 'o', 8);
hold on;
plot(C(:,1), C(:,2), 'kx', 'MarkerSize', 10, 'LineWidth', 2);
title('K-Means Clustering in MATLAB');
xlabel('Feature 1'); ylabel('Feature 2');
legend('Cluster 1', 'Cluster 2', 'Cluster 3', 'Centroids');
hold off;

Output: 

For larger or more complex datasets, using metrics like the silhouette coefficient or the Davies-Bouldin index helps assess the quality of clustering. 

The silhouette coefficient measures how similar each point is to its own cluster compared to other clusters, while the Davies-Bouldin index evaluates the compactness and separation of clusters. Both metrics provide insights into the effectiveness of the clustering algorithm, helping you choose the best clustering configuration.

Dive deeper into clustering methods with upGrad's free course in Unsupervised Learning: Clustering. This course will enhance your understanding of K Means Clustering MATLAB and its application to actual data analysis.

By understanding the syntax of the kmeans() function, preparing the data, and visualizing the results, you can gain insights from your dataset. 

Also Read: Supervised vs Unsupervised Learning: Difference Between Supervised and Unsupervised Learning

While these are the basics of K-Means clustering there are even more advanced techniques in MATLAB that are used to handle complex datasets and improve clustering performance. 

These will be explored in-depth later, but before that, let’s have a look at the various practical uses of K-Means clustering with MATLAB.

Practical Examples of K-Means Clustering Using MATLAB

K-Means clustering is best understood through practical, real applications. MATLAB’s powerful computational tools and built-in functions like kmeans() make it straightforward to apply the algorithm to diverse datasets. 

This section explores detailed examples of K-Means clustering in action, showcasing its utility in solving complex problems such as location optimization for businesses and analyzing renowned datasets like Iris. 

Through these examples of K means clustering, you’ll gain a hands-on understanding of the algorithm's implementation, visualization, and interpretation in MATLAB.

Also Read: 21 Best Ideas for MATLAB Projects & Topics For Beginners [2025]

McDonald’s Outlet Location Analysis

A fast-food chain like McDonald’s aims to optimize new outlet locations based on customer distribution. Using K-means clustering, you can identify high-density customer areas and strategically place new outlets to maximize accessibility.

Here is a step-by-step look at the process:

Step 1: Data Preparation

Prepare the customer location dataset, which includes geographical coordinates (latitude and longitude).

% Generate synthetic customer location data
rng(2); % Ensure reproducibility
customerLocations = [randn(100,2)*0.8 + [10, 20]; 
                    randn(80,2)*0.5 + [15, 25];
                    randn(60,2)*0.6 + [20, 15]]; % Three clusters

Step 2: Apply K-Means Clustering

Cluster customer locations into three groups to determine potential outlet areas.
% Number of clusters (outlets)
k = 3;
% Perform K-means clustering
[clusterIdx, clusterCenters] = kmeans(customerLocations, k, 'Replicates', 10);

Step 3: Visualization

Visualize the clusters and potential outlet locations.

% Scatter plot of customer clusters
figure;
gscatter(customerLocations(:,1), customerLocations(:,2), clusterIdx, 'rgb', 'o', 8);
hold on;
% Mark outlet locations (centroids)
plot(clusterCenters(:,1), clusterCenters(:,2), 'kx', 'MarkerSize', 15, 'LineWidth', 3);
title('Optimal Outlet Locations');
xlabel('Longitude');
ylabel('Latitude');
legend('Cluster 1', 'Cluster 2', 'Cluster 3', 'Proposed Outlets');

Results

• Clustered Locations: Customer groups are color-coded, indicating distinct high-density areas.
• Proposed Outlets: Centroids represent optimal outlet locations based on customer distribution.

Understand the various aspects behind consumer choices with upGrad's free course in Introduction to Consumer Behavior. Learn how clustering techniques, like K-means, can segment consumers and optimize marketing strategies.

Let’s have a look at another example next:

K-Means Clustering with the Iris Dataset

The Iris dataset contains measurements of flower species. K-means clustering can classify the flowers into species groups based on their features without prior labels.

Here are the required steps for this process:

Step 1: Load and Prepare Data

Load the Iris dataset, selecting features for clustering (e.g., petal length and width).

% Load Iris dataset
load fisheriris;
data = meas(:,3:4); % Petal length and width

Step 2: Perform K-Means Clustering

Cluster the data into three groups corresponding to the three Iris species.
% Number of clusters
k = 3;
% K-means clustering
[clusterIdx, clusterCenters] = kmeans(data, k, 'Replicates', 5);

Step 3: Visualization

Visualize the clustering results using a scatter plot.

% Plot clusters
figure;
gscatter(data(:,1), data(:,2), clusterIdx, 'rgb', 'o', 8);
hold on;
% Mark centroids
plot(clusterCenters(:,1), clusterCenters(:,2), 'kx', 'MarkerSize', 15, 'LineWidth', 3);
title('Iris Clustering Using K-Means');
xlabel('Petal Length');
ylabel('Petal Width');
legend('Cluster 1', 'Cluster 2', 'Cluster 3', 'Centroids');

Results

• Clustered Data: Groups represent different Iris species based on petal dimensions.
• Centroids: Mean values of petal length and width for each cluster.

While the practical examples of K means clustering show how it can be implemented in MATLAB for specific use cases, its versatility extends far beyond isolated examples. 

Also Read: MATLAB Data Types: Everything You Need to Know

Let’s explore the broader applications of K-means clustering in real business scenarios to understand its widespread impact.

Business Applications of K-Means Clustering MATLAB in Real-World Scenarios

K-means clustering is applied across diverse industries to solve clustering problems. Below is a table summarizing its use in various fields.

These practical examples and uses in various fields demonstrate the power of K-means clustering in MATLAB, making it an indispensable tool for data-driven decision-making.

Also Read: Top 15 Types of Data Visualization: Benefits and How to Choose the Right Tool for Your Needs in 2025

Having seen the practical examples of K-means clustering in MATLAB, it’s also important to understand its strengths and weaknesses. Next let’s have a look at the key advantages and limitations of using K-means clustering in MATLAB, helping you make more informed decisions about when and how to apply it effectively.

Advantages and Limitations of K-Means Clustering in MATLAB

MATLAB offers many advantages, like an intuitive interface, powerful computation, and excellent visualization tools, making it a favorite among data scientists. However, these strengths are balanced by limitations, such as handling non-spherical clusters, sensitivity to initialization, and challenges with large or high-dimensional datasets in complex scenarios.

Below is a detailed exploration of the strengths and weaknesses of K-means clustering in MATLAB, accompanied by potential solutions for overcoming these limitations.

Advantages of K-Means Clustering in MATLAB

K-means clustering in MATLAB offers simplicity, scalability, and speed, making it ideal for partitioning large datasets. It is easy to implement and can handle various types of data. Let's explore the key benefits it brings to clustering tasks.

1. Intuitive Implementation with the kmeans() Function

• MATLAB provides a built-in kmeans() function, making it easy to implement the algorithm with minimal coding.
• Customizable parameters, such as initialization methods ('Start') and the number of iterations ('MaxIter'), simplify algorithm tuning.

2. High Computational Efficiency

• MATLAB’s optimization ensures rapid convergence and efficient memory usage, even for medium-sized datasets.
• The option to use multiple replicates ('Replicates') minimizes the impact of poor centroid initialization.

3. Powerful Visualization Tools

• MATLAB’s plotting tools (e.g., gscatter for 2D data and scatter3 for 3D data) allow users to interpret cluster structures effectively.
• Cluster centroids can be visually marked, helping in decision-making and data analytics.

4. Flexibility in Distance Metrics

• MATLAB supports multiple distance metrics, such as Euclidean, city block, and cosine, allowing users to tailor clustering to specific data characteristics.

5. Support for Parallel Computing

• With the Parallel Computing Toolbox, MATLAB can distribute computations across multiple CPU cores, accelerating large-scale clustering.

While K-means clustering in MATLAB offers several advantages, it’s important to recognize that no algorithm is perfect. Despite its strengths, K-means comes with some limitations that can affect its performance under certain conditions. 

Also Read: Clustering vs Classification: Difference Between Clustering & Classification

Let’s now take a look at these challenges and explore potential solutions to overcome them.

Limitations of K-Means Clustering in MATLAB and Solutions

While K-means is efficient, it has limitations, such as sensitivity to initial centroids and difficulty handling non-spherical clusters. Let’s have a look at these challenges and discuss solutions to overcome them.

1. Sensitivity to Initialization

Poor centroid initialization can lead to suboptimal clustering results, especially with complex datasets.

Solutions:

• Multiple Initializations: Increase the number of replicates ('Replicates') to improve the likelihood of better clustering.

[clusterIdx, clusterCenters] = kmeans(data, k, 'Replicates', 20);

Advanced Initialization Methods: Implement k-means++ for smarter centroid placement. While MATLAB lacks a built-in k-means++ option, custom implementations or external libraries can be used to address this.

2. Requirement to Predefine the Number of Clusters (k)

• The algorithm requires k to be specified, which may not be known in advance.
• Incorrect values of k can lead to under- or over-clustering.

Solutions:

Elbow Method: Plot the within-cluster sum of squares (WCSS) against different k values to find the optimal number of clusters.

 wcss = [];
for i = 1:10
[~, ~, sumd] = kmeans(data, i);
wcss = [wcss; sum(sumd)];
End
plot(1:10, wcss, '-o');
title('Elbow Method');
xlabel('Number of Clusters');
ylabel('WCSS');

Silhouette Analysis: Use MATLAB’s silhouette function to evaluate cluster quality for varying values of k.

 silhouette(data, clusterIdx);

3. Poor Performance on Non-Spherical Data

• K-means assumes clusters are spherical and evenly sized, which is not always true.
• It struggles with elongated, overlapping, or irregularly shaped clusters.

Solutions:

Switch to Other Algorithms: Use algorithms like DBSCAN or Gaussian Mixture Models for non-spherical data.

Kernel K-Means: Transform the data into a higher-dimensional space using a kernel function before applying K-means. MATLAB’s Statistics and Machine Learning Toolbox supports custom kernel functions.

4. Impact of Outliers

• Outliers significantly influence cluster centroids, potentially skewing results.
• MATLAB’s kmeans() function does not have built-in outlier handling.

Solutions:

Preprocess Data: Remove or transform outliers using statistics before clustering.

 % Remove outliers using interquartile range
Q1 = quantile(data, 0.25);
Q3 = quantile(data, 0.75);
IQR = Q3 - Q1;
data = data(data > Q1 - 1.5*IQR & data < Q3 + 1.5*IQR, :);

Robust Clustering Variants: Implement robust K-means variants that assign lower weights to outliers, such as Trimmed K-means.

5. Challenges with High-Dimensional Data

• High-dimensional data complicates clustering due to the curse of dimensionality, reducing the reliability of distance metrics.

Solutions:

Dimensionality Reduction: Use techniques like Principal Component Analysis (PCA) in machine learning or t-SNE before applying K-means.

 [coeff, score] = pca(data);
reducedData = score(:,1:3); % Select top 3 components
[clusterIdx, clusterCenters] = kmeans(reducedData, k);

Feature Selection: Reduce irrelevant features by calculating feature importance scores.

After understanding the advantages and limitations of K-means clustering, it’s clear that while the algorithm is useful, there are areas where it can be further improved. To address these challenges and enhance its performance, advanced techniques can be applied. 

Learn the essential data concepts with upGrad's free course in Data Structures & Algorithms. Strengthen your foundation for implementing K-means clustering efficiently, especially for large and complex datasets.

Let’s explore some of these methods that can take K-means clustering to the next level in MATLAB.

Advanced K-Means Clustering Techniques in MATLAB

K-means clustering is a popular algorithm for grouping data, but it struggles with high-dimensional, large, or irregular datasets. MATLAB offers advanced techniques to enhance K-means, making it effective for complex clustering scenarios. 

Here's a look at these methods, their use cases, and practical MATLAB implementations.

1. Mini-Batch K-Means for Large Datasets

Mini-Batch K-Means, ideal for large datasets, processes small random data batches. This is done instead of the entire dataset, making it faster and more memory-efficient.

Advantages:

• Handles large datasets efficiently.
• Reduces computational cost without significant loss in accuracy.
• Suitable for real-time clustering in dynamic systems.

Implementation in MATLAB:
MATLAB does not have a built-in Mini-Batch K-Means function, but it can be implemented using custom code. Here's a step-by-step process:

1. Initialize Clusters: Randomly initialize cluster centroids.
2. Iterate Through Mini-Batches: Use random subsets of the data to update centroids.
3. Update Centroids: Use a weighted moving average to adjust centroids for each batch.

data = rand(10000, 3); % Simulated dataset with 10,000 points
batchSize = 100; % Define batch size
k = 5; % Number of clusters
maxIterations = 50;
% Initialize centroids randomly
centroids = data(randperm(size(data, 1), k), :);
for iter = 1:maxIterations
   batchIndices = randperm(size(data, 1), batchSize); % Select random batch
   batch = data(batchIndices, :); 
   % Assign data points in batch to nearest centroid
   [~, clusterIdx] = pdist2(centroids, batch, 'euclidean', 'Smallest', 1);
       % Update centroids
   for i = 1:k
       pointsInCluster = batch(clusterIdx == i, :);
       if ~isempty(pointsInCluster)
           centroids(i, :) = (1 - 0.1) * centroids(i, :) + 0.1 * mean(pointsInCluster, 1);
       end
   end
end

Result:

While Mini-Batch K-Means offers an efficient solution for large datasets, another powerful optimization technique is parallel processing. By distributing the computational load across multiple cores or GPUs, you can further enhance the speed and scalability of K-means clustering.

2. Parallel Processing for Speed Optimization

K-means clustering can be computationally intensive for large datasets or high-dimensional data. MATLAB’s parallel computing tools allow the workload to be distributed across multiple CPU cores or GPUs, significantly speeding up computation.

Advantages:

• Reduces clustering time for large datasets.
• Improves scalability in big data scenarios.

Implementation in MATLAB:
MATLAB supports parallel processing using the parpool and parfor commands. The built-in kmeans function can automatically leverage parallel processing.

3. Dimensionality Reduction for High-Dimensional Data

High-dimensional data often complicates K-means clustering due to the "curse of dimensionality," where distances between points become less meaningful. Dimensionality reduction techniques, including PCA or t-SNE, can simplify the data while preserving its essential structure.

Advantages:

• Improves clustering quality by reducing noise.
• Enhances visualization by projecting data into 2D or 3D space.

Implementation in MATLAB:

1. Use PCA for Dimensionality Reduction:

data = rand(500, 50); % Simulated high-dimensional dataset (500 points, 50 features)
[coeff, score, ~] = pca(data); % Perform PCA
reducedData = score(:, 1:3); % Retain top 3 principal components
k = 5; % Number of clusters
[clusterIdx, centroids] = kmeans(reducedData, k);

1. Visualize Clusters in Reduced Dimensions:

scatter3(reducedData(:,1), reducedData(:,2), reducedData(:,3), 10, clusterIdx, 'filled');
title('Clusters After PCA Dimensionality Reduction');
xlabel('PC1'); ylabel('PC2'); zlabel('PC3');

Result:

While dimensionality reduction addresses high-dimensional data, K-means struggles with non-spherical clusters. For such cases, alternative methods like Gaussian Mixture Models (GMM) can be used to capture complex cluster shapes and relationships better.

4. Handling Non-Spherical Clusters

Traditional K-means assume clusters are spherical, which may not hold true for all datasets. MATLAB’s variations, such as Gaussian Mixture Models (GMM), can better handle non-spherical cluster shapes.

Advantages:

• Captures more complex relationships in data.
• Provides probabilistic clustering results.

Implementation Using MATLAB’s GMM:

data = rand(500, 3); % Simulated dataset
k = 3; % Number of clusters
% Fit a GMM model
gm = fitgmdist(data, k);
% Assign clusters based on posterior probabilities
clusterIdx = cluster(gm, data);
scatter3(data(:,1), data(:,2), data(:,3), 10, clusterIdx, 'filled');
title('Non-Spherical Clusters Using GMM');
xlabel('Feature 1'); ylabel('Feature 2'); zlabel('Feature 3');

Result:

From Mini-Batch K-Means for large-scale data to dimensionality reduction and parallel processing, these methods address specific challenges, making clustering more accurate and efficient. 

While learning K-Means clustering in MATLAB is key, truly understanding it requires ongoing learning and practice. With upGrad, you’ll get expert guidance and hands-on projects to build confidence and take your skills to the next level.

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