Deep Multiple Clustering

Deep multiple clustering aims to uncover multiple, potentially overlapping, hidden structures within data using deep learning techniques. Current research focuses on developing robust algorithms that learn disentangled feature representations, often employing variational methods or data augmentation strategies to capture diverse perspectives within the data, and incorporating user preferences or weak supervision to guide the clustering process. This field is significant because it allows for a more nuanced understanding of complex datasets by revealing multiple interpretations, improving the accuracy and interpretability of unsupervised learning, and enabling more effective data analysis across various applications.