Clustering Friendly Representation

Clustering-friendly representation learning aims to create data representations that facilitate effective clustering, grouping similar data points together without relying on pre-defined labels. Current research focuses on developing novel algorithms and model architectures, such as contrastive learning methods and those incorporating expectation-maximization frameworks or prototypical learning, to improve the quality of these representations by enhancing intra-cluster compactness and inter-cluster separation. This field is significant because improved clustering techniques are crucial for various applications, including new intent discovery, fair clustering, and unsupervised person re-identification, ultimately leading to more accurate and insightful data analysis across diverse domains.