he silhouette coefficient is a measure of how well-separated clusters are in a clustering analysis. It provides a way to assess the quality of clustering by evaluating both the cohesion within clusters and the separation between clusters. The silhouette coefficient ranges from -1 to 1, with higher values indicating better-defined clusters. Here’s how the silhouette…
he Mahalanobis distance is a measure of the distance between a point and a distribution, taking into account the correlation between variables. It is often used in statistics and machine learning to identify outliers and to assess the dissimilarity between a data point and a distribution. The Mahalanobis distance is defined for a point (x)…
istance measures (or similarity measures, depending on the context) play a crucial role in clustering algorithms, as they determine the similarity or dissimilarity between data points. Here are some common distance measures used in clustering: The choice of distance measure depends on the nature of your data and the specific requirements of your clustering task.…
