Description

Abstract:

            An important question facing visualization methods is how to be both general and sup-port 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 privacy-aware data mining. Automated data collection and data mining

techniques have paved the way to making automated decisions. Effective at removing direct and indirect

discrimination biases in the original data set preserving data quality. Services in the information society allow for

automatic and routine collection of large amounts of data