Discovering Synchronized Subsets of Sequences: A Large Scale Solution.

ABSTRACT: Finding the largest subset of sequences (i.e., time series) that are correlated above a certain threshold, within large datasets, is of significant interest for computer vision and pattern recognition problems across domains, including behavior analysis, computational biology, neuroscience, and finance. Maximal clique algorithms can be used to solve this problem, but they are not scalable. We present an approximate, but highly efficient and scalable, method that represents the search space as a union of sets called ϵ-expanded clusters, one of which is theoretically guaranteed to contain the largest subset of synchronized sequences. The method finds synchronized sets by fitting a Euclidean ball on ϵ-expanded clusters, using Jung's theorem. We validate the method on data from the three disti

SUBMITTER: Sariyanidi E 

PROVIDER: S-EPMC7508311 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature