This decision depends on the specific context. When dealing with large or sparse vectors representing each molecule, dimensionality reduction can be advantageous. Reducing dimensions often helps eliminate unnecessary or correlated components, making it easier to compare data points; it is often used for visualization purposes. On the other hand, the compact representation resulting from dimensionality reduction can introduce artificial clusters, as distances between points may be shrunken compared to the original space, potentially distorting the true relationships in the data. In the case of fulvene, the molecule is relatively small, and the vectors used for representation are not very large. Therefore, clustering will be applied directly to the descriptors without any prior dimensional reduction. Dimensional reduction can still be used afterward to visualize how the clustering algorithm identified different groups within the dataset.