Year: 2011
Type: conference-paper
Abstract: An important question facing visualization methods is how to be both general and support open-ended exploratory analysis. In this paper, we propose a visualization approach that can on the one hand be applied to any (classification or association) rules, but that is suited to bringing out features of mined patterns that are especially important in discrimination-aware and privacyaware data mining. We define new interestingness measures for items and rules and show various ways in which these can help in highlighting information in interactive settings. We conclude by arguing how this approach can lead to a new generation of feedback and awareness tools.
Source: Lirias
Authors: Gao, Bo, Berendt, Bettina
Institution: KU Leuven
Language: English
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2402075310