diff --git a/inst/pages/clustering.qmd b/inst/pages/clustering.qmd
index b7b933b6..e60719ae 100644
--- a/inst/pages/clustering.qmd
+++ b/inst/pages/clustering.qmd
@@ -136,7 +136,7 @@ dend |> set("labels_cex", 0.8) |> plot()
 We can also visualize the clusters by projecting the data onto
 two-dimensional
 surface that captures the most variability in the data. In this example, we use
-multi-dimensional scaling (MDS) (see [@sec-unsupervised-ordinantion]).
+multi-dimensional scaling (MDS) (see [@sec-unsupervised-ordination]).
 
 ```{r hclust3}
 library(scater)