The disclosure relates to technology for identifying a root cause of anomalous behavior in a communications network. A key quality indicator (KQI) indicative of a performance level associated with a source is received in the communication network. The KQI includes a performance measurement value to identify a performance level of the source having the anomalous behavior. An improved KQI indicative of a level of degradation is calculated based on the anomalous behavior at the source by recovering the KQI to a historical value. Sources of the anomalous behavior are clustered into subsets according to the level of degradation based on the calculated KQI improvement. A global or partial degradation type of the root cause source in the subset having a severe level of degradation is determined using hypothesis testing, and a confidence value is provided for the accepted degradation type.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory computer-readable medium storing computer instructions for identifying a root cause of anomalous behavior in a communications network, that when executed by one or more processors, perform the steps of: receiving and storing a key quality indicator (KQI) indicative of a performance level associated with a source in the communication network, the KQI including a performance measurement value identifying the performance level of the source having the anomalous behavior; calculating an improved KQI indicative of a level of degradation resulting from the anomalous behavior at the source by recovering a current performance measurement value at the source to a baseline performance measurement value at the source; grouping the source into at least one of a first subset of sources and a second subset of sources according to the level of degradation at the source based on the calculated improved KQI; and determining a degradation type for the source grouped in the second subset based on execution of a statistical hypothesis test and computing a confidence value indicative of a confidence level in accepting the determined degradation type as a result of the statistical hypothesis test. 2. The non-transitory computer-readable medium of claim 1, wherein the instructions for calculating the improved KQI further comprises computer instructions for: measuring a resultant value of the anomalous behavior at the source; and providing the improved KQI in response to the current performance measurement value of the source having the level of degradation satisfying a threshold value being recovered to the baseline performance measurement value. 3. The non-transitory computer-readable medium of claim 1 further comprises computer instructions for: computing the level of degradation at the source according to a difference between the current performance measurement value of the KQI when the anomalous behavior is detected and the baseline performance measurement value of the KQI based on a statistical performance measurement value; comparing the computed level of degradation at the source to determine a probability identifying that the source as being the root cause of the detected anomalous behavior; and recovering the KQI at the source to remove the detected anomalous behavior by restoring the current performance measurement value of the KQI to the baseline performance measurement value of the KQI. 4. The non-transitory computer-readable medium of claim 3, wherein the improved KQI of the ith source is calculated according to S  ( i ) = KQI  SOURCE - RNC  recovery  ( i ) - KQI  SOURCE - RNC  outlier KQI  SOURCE - RNC  history - KQI  SOURCE - RNC  outlier , where RAN is a Radio Access Network and RNC is a Radio Network Controller. 5. The non-transitory computer-readable medium of claim 1, the grouping further comprising computer instructions for: clustering the first subset of sources in response to identifying the improved KQI as satisfying a threshold value; and clustering the second subset of sources in response to identifying the improved KQI as failing to satisfy the threshold value. 6. The non-transitory computer-readable medium of claim 5, wherein the clustering is determined according to a learning process executed according to min c , μ   ∑ i   s i - μ c i  2 2   subject   to   S j ≥ α , for   C j = 2 , where ciε{1,2} is an index of one of the first and second subsets for which the ith source belongs, μj is a centroid of one of the jth first and second subsets, and a is a threshold indicative of a severity of the level of degradation. 7. The non-transitory computer-readable medium of claim 5, wherein the clustering further comprises computer instructions for: computing the threshold value over a region defining a lower bound (lb) and an upper bound (up) with a step size (Δ), wherein the threshold value is determined by =lb+(i−1)Δ, i=1,2, . . . ,floor((up−lb)/Δ; clustering the source into the first subset of sources in response to a degradation score of the source being less than the threshold value and clustering the source into the second subset in response to the degradation score of the source being greater than or equal to the threshold value; and determining the threshold value that solves min   ∑ i , c i = 1   s i - μ 1  2 + ∑ j , c j = 2   s j - μ 2  2 . 8. The non-transitory computer-readable medium of claim 1, wherein the execution of the statistical hypothesis test comprises computer instructions for: constructing data sets for use in the statistical hypothesis test; and solving the statistical hypothesis test constructed using the data sets using a Student's T-test, where the hypothesis test is formulated to determine the degradation type at the source as one of a global degradation and a partial degradation. 9. The non-transitory computer-readable medium of claim 8, wherein the hypothesis test is formulated according to { H 0  :   x _ - y _ < ɛ H 1  :   x _ - y _ ≥ ɛ , where x and y are defined as mean data sets {xi} and {yi} and ε is a threshold value. 10. The non-transitory computer-readable medium of claim 1, wherein execution of the statistical hypothesis test comprises computer instructions for: computing the KQI in response to the current performance measurement value at the source of the second subset of sources being recovered to an averaged baseline performance measurement value; and applying a decision rule to determine the degradation type for the second subset of sources as one of global degradation and partial degradation. 11. The non-transitory computer-readable medium of claim 1, wherein the source is one of a Radio Access Network (RAN), a Server and servers in a cloud computing system. 12. An apparatus for identifying a root cause of anomalous behavior in a communications network, comprising: a receiver receiving a key quality indicator (KQI) indicative of a performance level associated with a source in the communication network, the KQI including a performance measurement value identifying the performance level of the source having the anomalous behavior; and a processor configured to calculate an improved KQI indicative of a level of degradation resulting from the anomalous behavior at the source by recovering a current performance measurement value at the source to a baseline performance measurement value at the source; group the source into at least one of a first subset of sources and a second subset of sources according to the level of degradation at the source based on the calculated improved KQI; and determine a degradation type for the source grouped in the second subset based on execution of a statistical hypothesis test and compute a confidence value indicative of a confidence level in accepting the determined degradation type as a result of the statistical hypothesis test. 13. The apparatus of claim 12, wherein the processor is further configured to: measure a resultant value of the anomalous behavior at the source; and provide the improved KQI in response to the current performance measurement value of the source having the level of degradation satisfying a threshold value being recovered to the baseline performance measurement value. 14. The apparatus of claim 12, wherein the receiver is further configured to: compute the level of degradation at the source according to a difference between the current performance measurement value of the KQI when the anomalous behavior is detected and the baseline performance measurement value of the KQI based on a statistical performance measurement value; compare the computed level of degradation at the source to determine a probability identifying that the source as being the root cause of the detected anomalous behavior; and recover the KQI at the source to remove the detected anomalous behavior by restoring the current performance measurement value of the KQI to the baseline performance measurement value of the KQI. 15. The apparatus of claim 14, wherein the improved KQI of the ith source is calculated according to S  ( i ) = KQI  RAN - RNC  recovery  ( i ) - KQI  RAN - RNC  outlier KQI  RAN - RNC  history - KQI  RAN - RNC  outlier , where RAN is a Radio Access Network and RNC is a Radio Network Controller. 16. The apparatus of claim 12, wherein the processor is further configured to: cluster the first subset of sources in response to identifying the improved KQI as satisfying a threshold value; and cluster the second subset of sources in response to identifying the improved KQI as failing to satisfy the threshold value. 17. The apparatus of claim 16, wherein the processor is configured to cluster the source into the first and second subsets according to a learning process executed as min c , μ  ∑ i   s i - μ c i  2 2   subject   to   S j ≥ α , for   C j = 2 , where ciε{1,2} is an index of one of the first and second subsets for which the ith source belongs, μj is a centroid of one of the jth first and second subsets, and a is a threshold indicative of a severity of the level of degradation. 18. The apparatus of claim 16, wherein the processor is further configured to: compute the threshold value over a region defining a lower bound (lb) and an upper bound (up) with a step size (Δ), wherein the threshold value is determined by =lb+(i−1)Δ, i=1,2, . . . ,floor((up−lb)/Δ; cluster the source into the first subset of sources in response to a degradation score of the source being less than the threshold value and cluster the source into the second subset in response to the degradation score of the source being greater than or equal to the threshold value; and determine the threshold value that solves min   ∑ i , c i = 1   s i - μ 1  2 + ∑ j , c j = 2   s j - μ 2  2 . 19. The apparatus of claim 12, wherein the processor is further configured to execute the statistical hypothesis test by: constructing data sets for use in the statistical hypothesis test; and solving the statistical hypothesis test constructed using the data sets using a Student's T-test, where the hypothesis test is formulated to determine the degradation type at the source as one of a global degradation and a partial degradation. 20. The apparatus of claim 19, wherein the hypothesis test is formulated according to { H 0  :   x _ - y _ < ɛ H 1  :   x _ - y _ ≥ ɛ , where x and y are defined as mean data sets {xi} and {yi} and ε is a threshold value. 21. The apparatus of claim 12, wherein the processor is configured to execute the statistical hypothesis test by: computing the KQI in response to the current performance measurement value at the source of the second subset of sources being recovered to an averaged baseline performance measurement value; and applying a decision rule to determine the degradation type for the second subset of sources as one of global degradation and partial degradation. 22. The apparatus of claim 12, wherein the source is one of a Radio Access Network (RAN) and a Server. 23. A method for identifying a root cause of anomalous behavior in a communications network, the method comprising: receiving a key quality indicator (KQI) indicative of a performance level associated with a source in the communication network, the KQI including a performance measurement value identifying the performance level of the source having the anomalous behavior; calculating an improved KQI indicative of a level of degradation resulting from the anomalous behavior at the source by recovering a current performance measurement value at the source to a baseline performance measurement value at the source; grouping the source into at least one of a first subset of sources and a second subset of sources according to the level of degradation at the source based on the calculated improved KQI; and determining a degradation type for the source grouped in the second subset based on execution of a statistical hypothesis test and computing a confidence value indicative of a confidence level in accepting the determined degradation type as a result of the statistical hypothesis test. 24. The method of claim 23 further comprises: computing the level of degradation at the source according to a difference between the current performance measurement value of the KQI when the anomalous behavior is detected and the baseline performance measurement value of the KQI based on a statistical performance measurement value; comparing the computed level of degradation at the source to determine a probability identifying that the source as being the root cause of the detected anomalous behavior; and recovering the KQI at the source to remove the detected anomalous behavior by restoring the current performance measurement value of the KQI to the baseline performance measurement value of the KQI. 25. The method of claim 24, wherein the improved KQI of the ith source is calculated according to S  ( i ) = KQI  SOURCE - RNC  recovery  ( i ) - KQI  SOURCE - RNC  outlier KQI  SOURCE - RNC  history - KQI  SOURCE - RNC  outlier , where RAN is a Radio Access Network and RNC is a Radio Network Controller.