Abstract: The performance of machine learning models in real-world applications is often challenged by data drift, in which the statistical properties of the data evolve over time. This phenomenon is ...

Model-based clustering provides a principled way of developing clustering methods. We develop a new model-based clustering methods for count data. The method combines clustering and variable selection ...

Abstract: As industrial processes become increasingly complex, the importance of data-driven industrial automation is becoming increasingly apparent. However, due to the difficulty of labeling target ...