Fast Generalized Subset Scan for Anomalous Pattern Detection

Edward McFowl; III; Skyler Speakman; Daniel B. Neill

We propose Fast Generalized Subset Scan (FGSS), a new method for detecting anomalous patterns in general categorical data sets. We frame the pattern detection problem as a search over subsets of data records and attributes, maximizing a nonparametric scan statistic over all such subsets. We prove that the nonparametric scan statistics possess a novel property that allows for efficient optimization over the exponentially many subsets of the data without an exhaustive search, enabling FGSS to scale to massive and high-dimensional data sets. We evaluate the performance of FGSS in three real-world application domains (customs monitoring, disease surveillance, and network intrusion detection), and demonstrate that FGSS can successfully detect and characterize relevant patterns in each domain. As compared to three other recently proposed detection algorithms, FGSS substantially decreased run time and improved detection power for massive multivariate data sets.

Fast Generalized Subset Scan for Anomalous Pattern Detection

Abstract