Description: K-means clustering performance metrics are used to evaluate how well the K-means algorithm has performed. This algorithm is an unsupervised learning technique that aims to divide a dataset into K groups or clusters, where each group consists of elements that are more similar to each other than to those in other groups. Performance metrics are essential for determining the quality of these groupings, as they allow for quantifying the internal cohesion of clusters and the separation between them. Among the most common metrics are the sum of squared distances within the cluster (inertia), the Silhouette index, which measures how similar an object is to its own cluster compared to other clusters, and the Davies-Bouldin index, which assesses the relationship between the distance between clusters and the dispersion within them. These metrics not only help in selecting the optimal number of clusters but also allow for comparing different runs of the algorithm and adjusting its parameters to improve results. In summary, performance metrics are fundamental tools for validating and optimizing the K-means clustering process, ensuring that the results are meaningful and useful for data analysis.
