Reframing in Clustering

Md Naimul Hoque, Chowdhury Farhan Ahmed, Nicolas Lachiche, Carson K. Leung, Hao Zhang

Adaptation of the dataset shift has grown to be of great importance in machine learning problems in recent years. Reframing has emerged as a new machine learning technique that adapts the context changes between training and target domains. One of the advantages of reframing is that it can offer good performances with a limited amount of deployment data. Reframing has already been implemented in classification and regression by reusing labelled training data with the help of few labelled target data. However, reframing in clustering is still a challenging research problem because of its unsupervised nature. In this paper, we concentrate on building a reframing method for clustering. We also show the necessity and effectiveness of our method in contrast to retraining, which is the process of learning new model in the testing and deployment phases. Our evaluation results with extensive experiments using both synthetic and real-life datasets show that our method correctly identifies most of the shifts between datasets and builds better clustering model than retraining.