Siluettipisteitä

Siluettipisteitä, also known as silhouette points, are a concept used in the analysis of clustering algorithms, particularly in evaluating the quality of the resulting clusters. A silhouette point measures how well each data point fits within its assigned cluster and how poorly it fits into neighboring clusters. The value of a silhouette point ranges from -1 to +1.

A silhouette point close to +1 indicates that the data point is well-clustered and far from any other cluster. A value near 0 suggests that the data point lies on or very close to the decision boundary between two neighboring clusters, making it ambiguous which cluster it truly belongs to. A silhouette point close to -1 means the data point is likely misclassified and might belong to a different cluster.

The average silhouette score for all data points in a dataset provides an overall measure of the clustering's quality. A higher average silhouette score indicates better-defined clusters. This metric is often used to compare different clustering algorithms or to determine the optimal number of clusters for a given dataset. For instance, if applying k-means clustering, one might calculate the average silhouette score for various values of 'k' and choose the 'k' that yields the highest score. It's a valuable tool for assessing the structure and separation of clusters identified by an algorithm.