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Using different metrics to assess cluster tightness is a valid strategy for improving a model's performance in the absence of ground truth labels. In unsupervised learning scenarios, where labeled data is not available, determining the quality of clustering can be challenging. Applying various clustering metrics—such as silhouette score, Davies-Bouldin index, or within-cluster sum of squares—allows practitioners to evaluate and compare the effectiveness of clustering configurations. This helps identify optimal clustering arrangements, ensuring the clusters formed reflect meaningful groupings of the data.

In contrast, while using deep learning techniques could enhance performance in some contexts, it generally requires a large amount of data and labeled examples. Feature selection methods can help streamline data inputs but do not inherently improve cluster quality without some form of validation using ground truth. Significantly increasing the number of clusters may lead to overfitting or create many noisy clusters, rendering the analysis less useful without a way to measure cluster performance effectively.