In all the examples that I can see online, people have used a labelled dataset. I however am stuck trying to construct a model to perform anomaly detection on unlabelled dataset (unsupervised anomaly detection).
Problem Statement: To separate the anomalous points from the normal ones. The features available with us are mem_usage, mem_total, cpu_usage_idle, cpu_usage_iowait, etc
Using the anomaly detection, I plan to label the dataset saying whether anomalous or not. A step further would be aggregate each row based on the server the data belongs to and find if the server is healthy, problematic. Even further would be predicting when a server might face a similar problem and thus suggest ways to mitigate the problem.
Any kind of resources (papers, code blogs, videos) are really welcome. I am just starting out and am open to trying out new stuff as well.