Discovery of Periodic Patterns in Sequence Data: A Variance-Based Approach

Abstract

We address the discovery of periodic patterns in sequence data. Building on prior work in this area, we present definitions and new methods for characterizing and identifying four types of periodic patterns. A unifying concept across the different types of periodic patterns we consider is the use of statistical variance to define periodicity. This lends itself to efficient variance-reduction algorithms for identifying periodic patterns. We motivate and test our approach using both extensive simulated sequences and real sequence data from online clickstream data.