Epsilon-closure for frequent pattern analysis

Abstract

Methods, systems, and devices supporting epsilon (ε)-closure for frequent pattern (FP) analysis are described. Some database systems may analyze data sets to determine FPs. In some cases, the FP set may include a large number of semi-redundant patterns, resulting in significant memory or processing overhead. To reduce the redundancy of these patterns, the database system may implement pre-configured or dynamic threshold occurrence differences (e.g., ε values) to test against related patterns. For example, the database system may calculate the difference between the data objects covered by a sub-pattern and a super-pattern (e.g., where the super-pattern includes all the same data attributes of the sub-pattern, plus one additional attribute). This difference may be compared to a corresponding ε value, and if the difference is less than the ε value, the database system may remove one of the patterns (e.g., the sub-pattern) from the set of valid FPs to limit redundancy.