283 research outputs found
Visual-Interactive Analysis With Self-Organizing Maps - Advances and Research Challenges
Based on the Self-Organizing Map (SOM) algorithm, development of effective solutions for visual analysis and retrieval in complex data is possible. Example application domains include retrieval in multimedia data bases, and analysis in financial, text, and general high-dimensional data sets. While early work defined basic concepts for data representation and visual mappings for SOM-based analysis, recent work contributed advanced visual representations of the output of the SOM algorithm, and explored innovative application concepts
Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs
The overview-driven visual analysis of large-scale dynamic graphs poses a
major challenge. We propose Multiscale Snapshots, a visual analytics approach
to analyze temporal summaries of dynamic graphs at multiple temporal scales.
First, we recursively generate temporal summaries to abstract overlapping
sequences of graphs into compact snapshots. Second, we apply graph embeddings
to the snapshots to learn low-dimensional representations of each sequence of
graphs to speed up specific analytical tasks (e.g., similarity search). Third,
we visualize the evolving data from a coarse to fine-granular snapshots to
semi-automatically analyze temporal states, trends, and outliers. The approach
enables to discover similar temporal summaries (e.g., recurring states),
reduces the temporal data to speed up automatic analysis, and to explore both
structural and temporal properties of a dynamic graph. We demonstrate the
usefulness of our approach by a quantitative evaluation and the application to
a real-world dataset.Comment: IEEE Transactions on Visualization and Computer Graphics (TVCG), to
appea
FDive: Learning Relevance Models using Pattern-based Similarity Measures
The detection of interesting patterns in large high-dimensional datasets is
difficult because of their dimensionality and pattern complexity. Therefore,
analysts require automated support for the extraction of relevant patterns. In
this paper, we present FDive, a visual active learning system that helps to
create visually explorable relevance models, assisted by learning a
pattern-based similarity. We use a small set of user-provided labels to rank
similarity measures, consisting of feature descriptor and distance function
combinations, by their ability to distinguish relevant from irrelevant data.
Based on the best-ranked similarity measure, the system calculates an
interactive Self-Organizing Map-based relevance model, which classifies data
according to the cluster affiliation. It also automatically prompts further
relevance feedback to improve its accuracy. Uncertain areas, especially near
the decision boundaries, are highlighted and can be refined by the user. We
evaluate our approach by comparison to state-of-the-art feature selection
techniques and demonstrate the usefulness of our approach by a case study
classifying electron microscopy images of brain cells. The results show that
FDive enhances both the quality and understanding of relevance models and can
thus lead to new insights for brain research.Comment: 12 pages, 7 figures, 2 tables, LaTeX; corrected typo; added DO
Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization
Topic models are a class of unsupervised learning algorithms for detecting
the semantic structure within a text corpus. Together with a subsequent
dimensionality reduction algorithm, topic models can be used for deriving
spatializations for text corpora as two-dimensional scatter plots, reflecting
semantic similarity between the documents and supporting corpus analysis.
Although the choice of the topic model, the dimensionality reduction, and their
underlying hyperparameters significantly impact the resulting layout, it is
unknown which particular combinations result in high-quality layouts with
respect to accuracy and perception metrics. To investigate the effectiveness of
topic models and dimensionality reduction methods for the spatialization of
corpora as two-dimensional scatter plots (or basis for landscape-type
visualizations), we present a large-scale, benchmark-based computational
evaluation. Our evaluation consists of (1) a set of corpora, (2) a set of
layout algorithms that are combinations of topic models and dimensionality
reductions, and (3) quality metrics for quantifying the resulting layout. The
corpora are given as document-term matrices, and each document is assigned to a
thematic class. The chosen metrics quantify the preservation of local and
global properties and the perceptual effectiveness of the two-dimensional
scatter plots. By evaluating the benchmark on a computing cluster, we derived a
multivariate dataset with over 45 000 individual layouts and corresponding
quality metrics. Based on the results, we propose guidelines for the effective
design of text spatializations that are based on topic models and
dimensionality reductions. As a main result, we show that interpretable topic
models are beneficial for capturing the structure of text corpora. We
furthermore recommend the use of t-SNE as a subsequent dimensionality
reduction.Comment: To be published at IEEE VIS 2023 conferenc
Industrial Production Process Improvement by a Process Engine Visual Analytics Dashboard
Digitalization reshapes production in a sense that production processes are required to be more flexible and more interconnected to produce products in smaller lot sizes. This makes the process improvement much more challenging, as traditional approaches, which are based on the learning curve, are difficult to apply. Data-driven technologies promise help in learning faster by making use of the massive data volumes collected in production environments. Visual analytics approaches are particularly promising in this regard as they aim to enable engineers with their rich domain knowledge to identify opportunities for process improvements. Based on the assumption that process improvement should be connected with the process engine managing the process execution, we propose a visual analytics dashboard which integrates process models. Based on a case study in the smart factory of Vienna, we conducted two pair analytics sessions. The first results seem promising, whereas domain experts articulate their wish for improvements and future work
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