The Iris dataset is exclusively used for supervised learning tasks, such as classification, and cannot be used for unsupervised learning methods like clustering.

The Iris dataset contains measurements of flower sepal and petal length and width for 150 samples, along with the species label, but the labels are optional. Because the data include features that can be grouped naturally, you can run clustering algorithms to see if the flowers form distinct groups without using the species names. For example, applying k‑means with three clusters often separates the three Iris species roughly, even though the algorithm never sees the labels. The dataset is popular for supervised learning because the species labels allow a clear training‑test split, but the same data can also reveal patterns in an unsupervised way. Therefore, the Iris dataset is not exclusively for supervised learning; it can also be used for clustering and other unsupervised methods.