Suppose you are working on a project that involves analyzing sensor data from industrial equipment to detect abnormal behavior or faults. Please answer the following questions based on this scenario: $($a$)(6$ marks$)$ If the objective is to detect anomalies $($faulty behavior$)$ in the sensor data without labeled examples of faults, should you use supervised or unsupervised learning? Explain your choice and discuss any limitations of the alternative approach. $($b$)(7$ marks$)$ Describe the flow of a specific unsupervised learning algorithm that could be used for anomaly detection in this context. $($c$)(7$ marks$)$ We know that there is a phenomenon called overfitting in supervised learning. So$,$ is there any overfitting phenomenon in unsupervised learning? If yes, please explain how to judge and propose solutions to address the overfitting. If not, please explain why?