To perform k-means clustering on a 3D matrix in MATLAB, you first need to reshape the matrix into a 2D format that the kmeans() function can work with. Here's a step-by-step guide:
- Reshape the 3D matrix into 2D: Flatten the 3D matrix so that each row represents a point in the 3D space.
- Apply k-means clustering: Use the kmeans() function on the reshaped data.
- Reshape the clustered labels back to 3D: This will give you a 3D matrix of cluster labels.
Here is an example code snippet:
data_reshaped = reshape(data, X*Y*Z, 1);
[idx, C] = kmeans(data_reshaped, k);
clustered_data = reshape(idx, X, Y, Z);
Notes:
- The reshape function is used to convert the 3D matrix into a 2D matrix where each row corresponds to a point in the 3D space.
- kmeans() is applied to the reshaped data.
- Finally, the cluster labels are reshaped back to the original 3D dimensions.
Make sure to adjust the number of clusters k to fit your specific needs.