Evaluating Clustering Algorithms Without Labels

This presentation explores clustering evaluation, beginning with an introduction to clustering and its significance in analysis. It delves into challenges such as lack of labeled data and subjectivity in quality assessment. Key internal evaluation techniques, including Silhouette Score, Davies–Bouldin Index, and Calinski–Harabasz Index, are discussed alongside additional methods like Dunn Index and the cohesion vs. separation concept. Limitations of these approaches are also addressed. The...