Abstract
This paper proposes a new method to generate edited topics or clusters to analyze images for prioritizing quality issues. The approach is associated with a new way for subject matter experts to edit the cluster definitions by “zapping” or “boosting” pixels. We refer to the information entered by users or experts as “high-level” data and we are apparently the first to allow in our model for the possibility of errors coming from the experts. The collapsed Gibbs sampler is proposed that permits efficient processing for datasets involving tens of thousands of records. Numerical examples illustrate the benefits of the high-level data related to improving accuracy measured by Kullback–Leibler (KL) distance. The numerical examples include a Tungsten inert gas example from the literature. In addition, a novel laser aluminum alloy image application illustrates the assignment of welds to groups that correspond to part conformance standards.
Highlights
Clustering is an informatics technique that allows practitioners to focus attention on a few important factors in a process
We applied Expert Refined Topic (ERT) modeling to two numerical examples in which the pixel grayscale images [44]
In the previous we applied ERT modeling to two numerical examples in which the ground undercut in Figuresection, 3
Summary
Clustering is an informatics technique that allows practitioners to focus attention on a few important factors in a process. As a result, clustering can provide a data-driven prioritization for quality issues relevant to allocating limited attention and resources. Informatics professionals are asked more than ever to be versant in using the information technology revolution [1,2,3]. This revolution exposed practitioners to large databases of images (and texts) that provided insights into quality issues. The practitioner generally has no systematic technique for analyzing the freestyle text or image. This is true even while the text or image clearly contains much relevant information for causal analysis [4,5]
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