Which clustering method can identify outliers effectively?

The clustering method that effectively identifies outliers is typically based on its ability to handle noise and data points that significantly deviate from the main cluster structures. K-means clustering is sensitive to outliers because it relies on the mean of the data points to form clusters. An outlier can disproportionately influence the mean, which may lead to misleading clusters. Hierarchical clustering also has limitations when it comes to identifying outliers, as it can create clusters based on the distance without specifically accounting for points that stray significantly from the main data groups.

Both methods tend to group data based on similarity and do not inherently separate outliers from the main dataset. Other methods, such as DBSCAN or Isolation Forest, are more adept at identifying outliers as they consider the density of points and can treat sparse regions, where outliers typically exist, differently.

Therefore, asserting that neither K-means nor hierarchical clustering effectively identify outliers accurately reflects their limitations in this context.