What question did this study set out to answer?

This research aims to improve the Disjunctive Anomaly Detection (DAD) algorithm for identifying complex disease outbreaks.

March 24, 2013Open Access

Searching for Complex Patterns Using Disjunctive Anomaly Detection

MSMaheshkumar SabhnaniCarnegie Mellon University ADArtur DubrawskiUniversity of Indianapolis JSJeff SchneiderThe University of Texas at San Antonio

Key Points

This research aims to improve the Disjunctive Anomaly Detection (DAD) algorithm for identifying complex disease outbreaks.
Extended DAD algorithm to manage various data distributions and cluster interactions.
Applied approximations to enable fast processing of complex datasets.
Compared DAD's performance against state-of-the-art methods like WSARE and LAS.
DAD outperformed state-of-the-art methods on large data sets typically found in biosurveillance.
Demonstrated the ability to effectively identify multiple clusters across diverse dimensions.

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.

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Cite This Study

Sabhnani et al. (Sun,) studied this question.

synapsesocial.com/papers/6a1254f3c031bb6829a657de https://doi.org/https://doi.org/10.5210/ojphi.v5i1.4611

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