IDENTIFYING OUTLIERS IN A LARGE SET OF OBJECTS

摘要

Described herein are various technologies pertaining to identifying global outlier candidates from a relatively large collection of computer-readable objects in a distributed computing environment. The collection of computer-readable objects is partitioned into a plurality of sets of objects, and local outlier candidates are identified from each set of objects in the plurality of sets of objects. The local outlier candidates are updated through a hierarchical pairwise similarity analysis until global outlier candidates are identified. Thereafter, a pairwise similarity analysis is undertaken with respect to the global outlier candidates and the sets of objects in the plurality of sets of objects to identify true global outliers.