Multiple Clustering

Multiple clustering aims to uncover multiple, distinct groupings within a single dataset, revealing diverse perspectives on the underlying data structure, unlike traditional methods that yield only a single clustering. Current research focuses on developing algorithms that automatically discover relevant clustering criteria, often leveraging deep learning architectures and incorporating multi-modal data (e.g., images and text) to improve interpretability and personalization. These advancements are significant for various applications, including analyzing large-scale datasets with complex structures and enabling more nuanced interpretations in fields like computer vision and healthcare, where multiple perspectives on the data are crucial.