Versions Compared

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.
Excerpt
Divbox
classittc-excerpt-

...

text

Conference Publishing

Dr Michael Stewart

...

Authors:

...

Michael Stewart

...

...


Divbox
classittc-sort-date

2022-12-

...

03

Publication

UI Text Box
size

...

Perth - Hyatt Regency Perth, Perth, WA, Australia

...

large
typetip

AI 2022: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13728. Springer, Cham

pp 311-324

Australasian Joint Conference on Artificial Intelligence
Stewart, M. (2022). QUARRY: A Graph Model for Queryable Association Rules. In: Aziz, H., Corrêa, D., French, T. (eds) AI 2022: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13728. Springer, Cham. https://doi.org/10.1007/978-3-031-22695-3_22Image Added

Quality Indicators

UI Text Box
sizelarge
typenote

Peer Reviewed

Relevance to the Centre

UI Text Box
sizelarge
Association rule mining is a pivotal technique for knowledge discovery

...

, but often involves time-intensive manual labour when performed on large datasets. In this paper we propose a solution for this problem: QUARRY, a graph

...

model that enables consumable and queryable insights from association rules. In contrast to existing systems

...

which take a list of rules and display them in a purpose-built visualisation,

...

our graph-based model enables association rules to be queried directly via graph queries

...

. Through a case study on maintenance data we show how this model enhances knowledge discovery by eliminating the need for domain experts to trawl through large lists of rules to find useful information. QUARRY, which is designed for compatibility with existing knowledge graphs, provides users with the means to easily search for rules pertaining to specific items as well as roll up and drill down on their searches

UI Button
color

...

blue
newWindowtrue
sizelarge

...

icon

...

label
title

...

DOI: https://doi.org/10.1007/978-3-031-22695-3_22
urlhttps://doi.org/https://

...

doi.org/10.1007/978-3-031-22695-3_22