Internal validity indices

Internal validity indices are tools used to evaluate how well a clustering algorithm has grouped data. They measure the quality of clusters based solely on the data itself, assessing factors like how close data points are within a cluster and how distinct different clusters are from each other. High indices indicate cohesive and well-separated clusters, suggesting that the grouping accurately reflects the structure of the data. These indices help determine the effectiveness of clustering without relying on external labels or known outcomes.