MSE Research Project Database

Decision Support with Uncertain Data


Project Leader: Uwe Aickelin
Primary Contact: Uwe Aickelin (uwe.aickelin@unimelb.edu.au)
Keywords: artificial intelligence; data mining; data structures; machine learning; optimisation
Disciplines: Computing and Information Systems
Domains:

Conventional data mining and statistical techniques work best for data that is certain. However, in the real world, uncertain data arises for many reasons such as imprecise measurement systems, natural variations, missing observations, human nature and linguistic expression. These uncertain data can be found across many problem domains. Such uncertainty is not always bad. Sometimes an uncertain measurement can be beneficial as it captures the ‘gold standard’, e.g. a range of expert opinions. The main step is to quantify and model uncertain data to optimise the datamining and hence decision support capabilities of the system.