Introducing new scaled algorithms for improved outlier detection

Outlier algorithms MAD and DBSCAN are effective in identifying hosts behaving differently from others, but may flag outliers among tightly grouped metrics due to their scale-independent nature. ScaledMAD and ScaledDBSCAN are newer scaled outlier algorithms that consider the relative scales of divergence and median data, making them more suitable for monitoring situations where dispersion in metrics is more meaningful in the context of overall magnitude. These algorithms can help detect anomalies within closely clustered groups of metrics when the overall scale of the metrics is large compared to the median distance between hosts and series.