After clustering a dataset, you notice that some data points are far from their respective cluster centroids. What might these points represent, and how can they be addressed?

Data points that are far from their cluster centroids are likely outliers. Outliers can significantly impact clustering results. To address this issue, you can consider different strategies such as removing outliers, using robust clustering algorithms, or applying feature scaling and normalization to make the clusters less sensitive to outliers.