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Exceptional contrast set mining: moving beyond the deluge of the obvious

Version 2 2024-06-06, 02:45
Version 1 2017-04-06, 12:03
conference contribution
posted on 2024-06-06, 02:45 authored by D Nguyen, Wei LuoWei Luo, D Phung, Svetha VenkateshSvetha Venkatesh
Data scientists, with access to fast growing data and computing power, constantly look for algorithms with greater detection power to discover “novel” knowledge. But more often than not, their algorithms give them too many outputs that are either highly speculative or simply confirming what the domain experts already know. To escape this dilemma, we need algorithms that move beyond the obvious association analyses and leverage domain analytic objectives (aka. KPIs) to look for higher order connections. We propose a new technique Exceptional Contrast Set Mining that first gathers a succinct collection of affirmative contrast sets based on the principle of redundant information elimination. Then it discovers exceptional contrast sets that contradict the affirmative contrast sets. The algorithm has been successfully applied to several analytic consulting projects. In particular, during an analysis of a state-wide cancer registry, it discovered a surprising regional difference in breast cancer screening.

History

Volume

LNAI 9992

Pagination

455-468

Location

Hobart, Tas.

Start date

2016-12-05

End date

2016-12-08

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319501260

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2016, Springer International Publishing AG

Editor/Contributor(s)

Ho Kang B, Bai Q

Title of proceedings

AI 2016 : Advances in artificial intelligence : Proceedings of the 29th Australian Joint Conference

Event

Australian Computer Society for AI. Conference (29th : 2016 : Hobart, Tasmania)

Publisher

Springer International

Place of publication

Cham, Switzerland

Series

Australian Computer Society for AI Conference