Ground Truth vs. Pattern Search

Absolutely. This is often referred to as Semi-Supervised Learning or hybrid pipelining. A common workflow involves using unsupervised learning (Clustering) to group raw data, then using manual ground-truth labels for 10% of those groups to propagate categories to the remaining data. This drastically reduces labeling costs while maintaining supervised precision.

Computational expense depends on the algorithm's objective. Supervised models often require intensive back-propagation (training) but are very fast during inference. Unsupervised methods, particularly those involving hierarchical clustering or large-scale dimensionality reduction (like t-SNE), can be exponentially more demanding during the initial discovery phase as they compare every data point to every other point.

Unlike supervised models that use Accuracy or F1-Scores against a key, unsupervised models rely on Internal Validation. We look at metrics like the Silhouette Coefficient (how similar an object is to its own cluster compared to others) or the Davies-Bouldin Index. Ultimately, the "success" is often qualitative: did the algorithm surface a pattern that provides a new academic or business insight?