Searching for Complex Patterns Using Disjunctive Anomaly Detection

Authors

  • Maheshkumar Sabhnani Carnegie Mellon University
  • Artur Dubrawski Carnegie Mellon University
  • Jeff Schneider Carnegie Mellon University

DOI:

https://doi.org/10.5210/ojphi.v5i1.4611

Abstract

We extend Disjunctive Anomaly Detection (DAD) algorithm to handle various data distributions and models of cluster interactions. It enables efficient searching and explanation of multiple disease outbreaks occurring simultaneously. Detected clusters can span multiple values along multiple dimensions, and can impact any subset of dimensions in data. This type of search is known to be exponentially complex, so DAD uses approximations to enable fast processing of large data. We demonstrate DAD's ability to systematically outperform state-of-art alternatives including What's Strange About Recent Events (WSARE) and Large Average Submatrix (LAS) on data of scales and complexities typically encountered in biosurveillance applications.

Author Biography

Maheshkumar Sabhnani, Carnegie Mellon University

Maheshkumar (Robin) Sabhnani is a PhD student at Machine Learning Department, Carnegie Mellon University. He is associated with the Auton Lab led by Artur Dubrawski and Jeff Schneider. He is interested in developing efficient algorithms to handle large-scale multidimensional data and to find interesting patterns in it. His algorithm, Disjunctive Anomaly Detection, can be applied to a wide variety of biosurveillance data to detect emerging outbreaks of disease that escape detection by more traditional algorithms.

Downloads

Published

2013-03-24

How to Cite

Sabhnani, M., Dubrawski, A., & Schneider, J. (2013). Searching for Complex Patterns Using Disjunctive Anomaly Detection. Online Journal of Public Health Informatics, 5(1). https://doi.org/10.5210/ojphi.v5i1.4611

Issue

Section

Oral Presentations: Cluster Detection