Evaluating the Quality of Clustering Results

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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.

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To evaluate the quality of clustering results, it is essential to introduce objective evaluation metrics that assess the rationality of clustering outcomes. Clustering performance evaluation methods can generally be classified into three categories: external evaluation, internal evaluation, and relative evaluation. External evaluation methods assess clustering by comparing generated cluster labels with known ground-truth labels, though this requires the dataset samples to have pre-existing class annotations. For comprehensive clustering assessment, internal evaluation methods (such as silhouette coefficient calculation using sklearn.metrics.silhouette_score) and relative evaluation methods (like elbow method visualization with sklearn.cluster.KMeans) should also be considered alongside external validation techniques.

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