Semi-automated Quality Assurance for Domain-Expert-Driven Data Exploration - An Application to Principal Component Analysis

Processing and exploring large quantities of electronic data is often a particularly interesting but yet challenging task. Both the lack of statistical and mathematical skills and the missing know-how of handling masses of (health) data constitute high barriers for profound data exploration – especially when performed by domain experts. This paper presents guided visual pattern discovery, by taking the well-established data mining method Principal Component Analysis as an example. Without guidance, the user has to be conscious about the reliability of computed results at any point during the analysis (GIGO-principle). In the course of the integration of principal component analysis into an ontology-guided research infrastructure, we include a guidance system supporting the user through the separate analysis steps and we introduce a quality measure, which is essential for profound research results.

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