Final answer:
For an astronomer with predefined image categories, classification is more appropriate than clustering. Classification assigns new instances to known categories, which suits the situation where hundreds of images have been manually categorized. Citizen science projects have also proved useful in similar scenarios involving large datasets.
Step-by-step explanation:
For the astronomer who wants to use analytics to automatically determine the category that each new image belongs to, the appropriate technique would be classification. Classification is used when the categories (or classes) are already known and one aims to assign new instances to one of these predefined categories. On the other hand, clustering is useful when there is no prior knowledge of the categories, and the goal is to group similar instances based on data-driven insights. Considering the astronomer has already categorized hundreds of images by hand, the use of classification algorithms would be more suitable to automate this process for new images. Techniques such as nearest neighbor analysis might also be relevant in assessing the spatial distribution of astronomical objects if the problem domain includes such analysis. The use of citizen science projects like the Galaxy Zoo and Spacewarps has shown that crowdsourcing can also be an effective way to handle large datasets by leveraging the human ability to recognize patterns in data, especially in cases where computer algorithms struggle.