1. A machine-implemented method comprising: automatically identifying by way of at least one of one or more processors, a finite set of detected anomalies within an anomalies distribution space having one or more feature-representing dimensional axes, the identified finite set of detected anomalies being derived from one or more data streams of a data processing system when the data streams report on measured parameters of the data processing system that violate system normalcy rules and are thus deemed as anomalous parameters; automatically performing, by way of at least one of said one or more processors, an attempt at aggregating together two or more of the detected anomalies within the anomalies distribution space for thereby forming, in response to the attempt at aggregating being successful, a corresponding anomalies reporting alert corresponding to the aggregated two or more of the detected anomalies; before performing the attempt at aggregating together said two or more of the detected anomalies, automatically identifying by way of a computationally less intensive variance analysis and by using at least one of said one or more processors, outlier anomalies within the finite set that are least clustered together within the anomalies distribution space with others of the anomalies in the finite set; not performing the attempt at aggregation on the identified outlier anomalies thereby avoiding placing corresponding workload on the at least one of said one or more processors that perform said attempt at aggregating together two or more of the detected anomalies; and automatically forming by way of at least one of said one or more processors, corresponding anomaly reporting alerts for respective ones of the identified outlier anomalies.