Close menu


Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

Hidden Markov Models for Surprising Pattern Detection in Discrete Symbol Sequence Data

McGarry, Kenneth (2022) Hidden Markov Models for Surprising Pattern Detection in Discrete Symbol Sequence Data. In: AI-2022 Forty-second SGAI International Conference on Artificial Intelligence, 13th-15th DECEMBER 2022, Cambridge. (In Press)

Item Type: Conference or Workshop Item (Paper)


Detecting unusual or interesting patterns in discrete symbol sequences is of great importance. Many domains consist of discrete sequential time-series such as internet traffic, online transactions, cyber-attacks, financial transactions, biological transcription, intensive care data and social sciences data such as career trajectories or residential history. The sequences usually consist of discrete symbols that may form regular patterns or motifs. We use regular expressions to construct the longest repeating sequences and sub-sequences that compose them, we then define these as motifs (which may or may not represent novel patterns). The sequences are now composed of simpler motifs which are used to build Hidden Markov Models models which can capture complex relationships based on location, frequency of occurrence and position. New data that deviates from established motifs either in location of appearance, frequency of appearance, or motif composition may represent patterns that may be different in some way and hence interesting to the user.

PDF (conference paper)
McGarry-paper269.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (694kB) | Preview

More Information

Uncontrolled Keywords: Motif, Regex, Sequence, Hidden Markov Model
Depositing User: Kenneth McGarry


Item ID: 15148
Official URL:

Users with ORCIDS

ORCID for Kenneth McGarry: ORCID iD

Catalogue record

Date Deposited: 06 Oct 2022 11:22
Last Modified: 06 Oct 2022 11:22


Author: Kenneth McGarry ORCID iD
Author: Kenneth McGarry

University Divisions

Faculty of Technology


Computing > Data Science
Computing > Artificial Intelligence

Actions (login required)

View Item (Repository Staff Only) View Item (Repository Staff Only)