In a host device, a method for performing an anomaly analysis of a computer environment includes applying a learned behavior function to a data training set and to a set of data elements received from at least one computer environment resource to define at least one learned behavior boundary relative to at least one cluster of data elements of the data training set; applying a sensitivity function to the at least one cluster to define a sensitivity boundary relative to at least one learned behavior boundary, the sensitivity boundary related to a variance associated with the at least one cluster and to a mean value of the at least one cluster; and identifying a data element of the set of data elements as an anomalous data element when the data element of the set of data falls outside of the sensitivity boundary.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. In a host device, a method for performing an anomaly analysis of a computer environment, comprising: applying, by host device, a learned behavior function to a data training set and to a set of data elements received from at least one computer environment resource to define at least one learned behavior boundary relative to at least one cluster of data elements of the data training set, the at least one learned behavior boundary related to a variance associated with the at least one cluster; applying, by host device, a sensitivity function to the at least one cluster to define a sensitivity boundary relative to at least one learned behavior boundary, the sensitivity boundary related to the variance associated with the at least one cluster and to a mean value of the at least one cluster; and identifying, by host device, a data element of the set of data elements as an anomalous data element associated with an attribute of the at least one computer environment resource when the data element of the set of data falls outside of the sensitivity boundary. 2. The method of claim 1, wherein the learned behavior function defines the learned behavior boundary as being three standard deviations from a centroid of the at least one cluster. 3. The method of claim 1, wherein the sensitivity boundary further relates to a ratio of the mean value of the at least one cluster and the variance of the at least one cluster. 4. The method of claim 1, wherein applying the sensitivity function to the at least one cluster further comprises adjusting, by the host device, a value of the sensitivity boundary for a relatively small mean value of the at least one cluster. 5. The method of claim 1, further comprising: receiving, by the host device, a user-selected global sensitivity parameter; and adjusting, by the host device, a sensitivity adjustment value of the sensitivity boundary based upon the global sensitivity parameter. 6. The method of claim 5, wherein receiving the global sensitivity parameter based upon the user selected input value comprises: displaying, by the host device and via a graphical user interface, a sensitivity selection screen; and receiving, by host device, the global sensitivity parameter based upon a user-selected input value provided from the sensitivity selection screen. 7. The method of claim 1, further comprising: receiving, by the host device, the set of data elements from the at least one computer environment resource of the computer infrastructure, each data element of the set of data elements relating to an attribute of the at least one computer environment resource; and applying, by host device, a clustering function to the set of data elements to define the data training set. 8. The method of claim 1, wherein the sensitivity function satisfies the following relation: τ i * = τ ±  δ   ( γ   μ  μ τ + β ( 1 - μ / α ) ) wherein τ* relates to a sensitivity boundary value, τ relates to the variance of the at least one cluster 82, δ relates to a user-selected global sensitivity parameter, γ related to an internal sensitivity parameter, μ relates to the mean value of the at least one cluster, α relates to a slope parameter configured to define a shape of the sensitivity boundary for a relatively small mean value, and β relates to an intercept parameter configured to define a value of the sensitivity boundary for a zero mean value. 9. A host device, comprising: a controller comprising a memory and a processor, the controller configured to: apply a learned behavior function to a data training set and to a set of data elements received from at least one computer environment resource to define at least one learned behavior boundary relative to at least one cluster of data elements of the data training set, the at least one learned behavior boundary related to a variance associated with the at least one cluster; apply a sensitivity function to the at least one cluster to define a sensitivity boundary relative to at least one learned behavior boundary, the sensitivity boundary related to the variance associated with the at least one cluster and to a mean value of the at least one cluster; and identify a data element of the set of data elements as an anomalous data element associated with an attribute of the at least one computer environment resource when the data element of the set of data falls outside of the sensitivity boundary. 10. The host device of claim 9, wherein the learned behavior function defines the learned behavior boundary as being three standard deviations from a centroid of the at least one cluster. 11. The host device of claim 9, wherein the sensitivity boundary further relates to a ratio of the mean value of the at least one cluster and the variance of the at least one cluster. 12. The host device of claim 9, wherein when applying the sensitivity function to the at least one cluster, the host device is further configured to adjust a value of the sensitivity boundary for a relatively small mean value of the at least one cluster. 13. The host device of claim 9, wherein the host device if further configured to: receive a user-selected global sensitivity parameter; and adjust a sensitivity adjustment value of the sensitivity boundary based upon the global sensitivity parameter. 14. The host device of claim 13, wherein when receiving the global sensitivity parameter based upon the user selected input value, the host device is configured to: display, via a graphical user interface, a sensitivity selection screen; and receive the global sensitivity parameter based upon a user-selected input value provided from the sensitivity selection screen. 15. The host device of claim 9, wherein the host device is further configured to: receive the set of data elements from the at least one computer environment resource of the computer infrastructure, each data element of the set of data elements relating to an attribute of the at least one computer environment resource; and apply a clustering function to the set of data elements to define the data training set. 16. The host device of claim 9, wherein the sensitivity function satisfies the following relation: τ i * = τ ±  δ   ( γ   μ  μ τ + β ( 1 - μ / α ) ) wherein τ* relates to a sensitivity boundary value, τ relates to the variance of the at least one cluster 82, δ relates to a user-selected global sensitivity parameter, δ related to an internal sensitivity parameter, μ relates to the mean value of the at least one cluster, α relates to a slope parameter configured to define a shape of the sensitivity boundary for a relatively small mean value, and β relates to an intercept parameter configured to define a value of the sensitivity boundary for a zero mean value. 17. A computer program product encoded with instructions that, when executed by a controller of a host device, causes the controller to: apply a learned behavior function to a data training set and to a set of data elements received from at least one computer environment resource to define at least one learned behavior boundary relative to at least one cluster of data elements of the data training set, the at least one learned behavior boundary related to a variance associated with the at least one cluster; apply a sensitivity function to the at least one cluster to define a sensitivity boundary relative to at least one learned behavior boundary, the sensitivity boundary related to the variance associated with the at least one cluster and to a mean value of the at least one cluster; and identify a data element of the set of data elements as an anomalous data element associated with an attribute of the at least one computer environment resource when the data element of the set of data falls outside of the sensitivity boundary.