Which of the following is NOT a benefit of clustering?

The benefit of clustering primarily lies in its ability to group similar data points together based on their characteristics. This process allows for the discovery of hidden patterns in data, which can provide insights that may not be immediately apparent. Clustering is also valuable in improving data classification performance, as it can help to segregate classes and make it easier to model each group effectively. Additionally, the results of clustering can facilitate simpler data visualization by allowing the representation of complex datasets in a more manageable format.

Reducing the dimensionality of datasets, however, is not a direct benefit of clustering. Rather, it is typically achieved through techniques such as Principal Component Analysis (PCA) or t-distributed Stochastic Neighbor Embedding (t-SNE), which are specifically designed to reduce the number of features in a dataset while preserving essential information. Clustering does not inherently reduce dimensionality; instead, it organizes data based on the similarities in the existing dimensions. Therefore, this option stands out as not being a core benefit of clustering.