Description: Constrained K-means clustering is an extension of the classic K-means algorithm that incorporates additional conditions into the clustering process. While traditional K-means aims to divide a dataset into K groups based solely on the distance between data points and centroids, constrained K-means introduces limitations that can be of the ‘must-link’ (should be in the same group) or ‘cannot-link’ (should not be in the same group) type. These constraints allow the algorithm to be more flexible and tailored to situations where prior knowledge about the relationships between data exists. For example, in various applications, where data may need to be grouped according to certain characteristics or labels, this approach ensures that clustering respects those predefined relationships. Key features of this method include the ability to handle unlabeled data while incorporating constraints, making it useful in contexts where prior information is valuable. Additionally, constrained K-means can enhance the quality of the formed groups by ensuring that desired relationships between data are maintained during the clustering process. In summary, this approach combines the simplicity of K-means with the complexity of relationships between data, providing a powerful tool for data analysis in various applications.
