A New Burrows Wheeler Transform Markov Distance


  • Edward Raff Booz Allen Hamilton
  • Charles Nicholas University of Maryland, Baltimore County
  • Mark McLean Laboratory for Physical Sciences




Prior work inspired by compression algorithms has described how the Burrows Wheeler Transform can be used to create a distance measure for bioinformatics problems. We describe issues with this approach that were not widely known, and introduce our new Burrows Wheeler Markov Distance (BWMD) as an alternative. The BWMD avoids the shortcomings of earlier efforts, and allows us to tackle problems in variable length DNA sequence clustering. BWMD is also more adaptable to other domains, which we demonstrate on malware classification tasks. Unlike other compression-based distance metrics known to us, BWMD works by embedding sequences into a fixed-length feature vector. This allows us to provide significantly improved clustering performance on larger malware corpora, a weakness of prior methods.




How to Cite

Raff, E., Nicholas, C., & McLean, M. (2020). A New Burrows Wheeler Transform Markov Distance. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 5444-5453. https://doi.org/10.1609/aaai.v34i04.5994



AAAI Technical Track: Machine Learning