Identification of New Clusters from Labeled Data Using Mixture Models - Kim, Yujung; Kim, Jaejik (Journal of computational biology, 2022)
Nowadays attempts to segment classes or groups are often found in various fields. Especially, one of emerging issues in biological and medical areas is identification of new subtypes of biological samples or patients. For the identification, we often need to find new subtypes from known classes. In such cases, we usually use clustering techniques. However, usual clustering methods could mix up the labels of the known classes in clustering outcomes and it might lead to wrong interpretation for the identified clusters. Also, they do not use the information about known classes. Thus, this study proposes a Gaussian mixture model-based approach for identifying new clusters from known classes while it maintains them. The performance of the proposed model is verified through simulations and it is applied to a breast cancer data set.