Semi Supervised Clustering

Semi-supervised clustering leverages a small amount of labeled data to improve the accuracy and efficiency of clustering large, unlabeled datasets. Current research focuses on developing algorithms that effectively integrate various types of prior knowledge, such as pairwise constraints or monotonicity constraints, into established clustering methods like spectral clustering, differential evolution, and structural entropy approaches. These advancements are significant because they reduce the need for extensive manual labeling, improving the scalability and applicability of clustering techniques across diverse domains, including e-commerce product matching and biological data analysis.