Clustering validity

Clustering validity refers to how well a clustering algorithm has grouped similar items together while keeping dissimilar items apart. It assesses the quality of clusters created from data by examining their coherence and separation. Good clustering should show high internal similarity (items in the same group are alike) and strong external differences (items in different groups are unlike). Validity measures help determine if a certain number of clusters is appropriate and if the clustering results make sense based on the intended analysis or application, ensuring meaningful insights from the data.