Group clustering using inter-group dissimilarities

A non-transitory computer readable medium comprising instructions to be executed in a computer system, wherein the instructions when executed in the computer system perform a method for improving performance of one or more machines within a system, the method comprising:

• forming a multidimensional dataset having a plurality of groups, each group of the plurality of groups corresponding to one of a plurality of machines within the system, the multidimensional dataset formed using performance related data associated with each of the plurality of machines;
• clustering the plurality of groups into one or more clusters based on intergroup dissimilarities between the plurality of groups, the intergroup dissimilarities determined by comparing performance related data associated with each group in the plurality of groups against performance related data associated with every other group in the plurality of groups;
• identifying one or more anomalous clusters from among the one or more clusters, the one or more anomalous clusters containing at least one group dissimilar to other groups in the plurality of groups;
• identifying the one or more anomalous groups in the one or more anomalous clusters;
• and
• adjusting a configuration of the system to improve the performance of the one or more machines corresponding to the one or more anomalous groups.

A computer system comprising:

• a memory;
• and
• a processor communicatively coupled to the memory, wherein the processor is configured to:
• form a multidimensional dataset having a plurality of groups, each group of the plurality of groups corresponding to one of a plurality of machines within the system, the multidimensional dataset formed using performance related data associated with each of the plurality of machines;
• cluster the plurality of groups into one or more clusters based on intergroup dissimilarities between the plurality of groups, the intergroup dissimilarities determined by comparing performance related data associated with each group in the plurality of groups against performance related data associated with every other group in the plurality of groups;
• identify one or more anomalous clusters from among the one or more clusters, the one or more anomalous clusters containing at least one group dissimilar to other groups in the plurality of groups;
• identify the one or more anomalous groups in the one or more anomalous clusters;
• and
• adjust a configuration of the system to improve the performance of the one or more machines corresponding to the one or more anomalous groups.