1 research outputs found
Visual Pattern-Driven Exploration of Big Data
Pattern extraction algorithms are enabling insights into the ever-growing
amount of today's datasets by translating reoccurring data properties into
compact representations. Yet, a practical problem arises: With increasing data
volumes and complexity also the number of patterns increases, leaving the
analyst with a vast result space. Current algorithmic and especially
visualization approaches often fail to answer central overview questions
essential for a comprehensive understanding of pattern distributions and
support, their quality, and relevance to the analysis task. To address these
challenges, we contribute a visual analytics pipeline targeted on the
pattern-driven exploration of result spaces in a semi-automatic fashion.
Specifically, we combine image feature analysis and unsupervised learning to
partition the pattern space into interpretable, coherent chunks, which should
be given priority in a subsequent in-depth analysis. In our analysis scenarios,
no ground-truth is given. Thus, we employ and evaluate novel quality metrics
derived from the distance distributions of our image feature vectors and the
derived cluster model to guide the feature selection process. We visualize our
results interactively, allowing the user to drill down from overview to detail
into the pattern space and demonstrate our techniques in a case study on
biomedical genomic data.Comment: Preprint - BDVA201