Evaluating the Quality of Clustering Results

To assess the quality of clustering results, objective evaluation metrics are required to validate the rationality of clustering outcomes. Clustering performance evaluation methods are typically categorized into three types: external evaluation, internal evaluation, and relative evaluation. External evaluation compares generated cluster labels with known ground-truth labels, but this approach assumes the dataset has pre-existing class labels. Implementation often involves metrics like Adjusted Rand Index or F-measure calculated through sklearn.metrics.cluster module.