Example Concept: Unsupervised clustering methods, such as K-means or hierarchical clustering, group data points based on similarity measures. A common pitfall is assuming that clusters are distinct and meaningful without validating them against domain knowledge or additional data. Clusters may represent noise or be influenced by the choice of distance metrics, the number of clusters, or the initial conditions of the algorithm.