Visual exploration of microbial populations

Fernstad, Sara Johansson; Johansson, Jimmy; Adams, Suzi; Shaw, Jane; Taylor, David R.

doi:10.1109/biovis.2011.6094057

Cited by 11 publications

(12 citation statements)

References 33 publications

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“…In order to lay out these glyphs in the visualization, multidimensional scaling is used based on the distances between the dimensions. Fernstad et al [7] demonstrate their quality metric based reduction in the analysis of high-dimensional datasets involving microbial populations.…”

Section: Related Workmentioning

confidence: 99%

Representative Factor Generation for the Interactive Visual Analysis of High-Dimensional Data

Turkay

Lundervold

Hauser

2012

IEEE Trans. Visual. Comput. Graphics

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Abstract-Datasets with a large number of dimensions per data item (hundreds or more) are challenging both for computational and visual analysis. Moreover, these dimensions have different characteristics and relations that result in sub-groups and/or hierarchies over the set of dimensions. Such structures lead to heterogeneity within the dimensions. Although the consideration of these structures is crucial for the analysis, most of the available analysis methods discard the heterogeneous relations among the dimensions. In this paper, we introduce the construction and utilization of representative factors for the interactive visual analysis of structures in high-dimensional datasets. First, we present a selection of methods to investigate the sub-groups in the dimension set and associate representative factors with those groups of dimensions. Second, we introduce how these factors are included in the interactive visual analysis cycle together with the original dimensions. We then provide the steps of an analytical procedure that iteratively analyzes the datasets through the use of representative factors. We discuss how our methods improve the reliability and interpretability of the analysis process by enabling more informed selections of computational tools. Finally, we demonstrate our techniques on the analysis of brain imaging study results that are performed over a large group of subjects.Index Terms-Interactive visual analysis, high-dimensional data analysis.

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Section: Related Workmentioning

confidence: 99%

Representative Factor Generation for the Interactive Visual Analysis of High-Dimensional Data

Turkay

Lundervold

Hauser

2012

IEEE Trans. Visual. Comput. Graphics

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“…The visually guided variable ordering and filtering reduces the complexity of the data and provides the user a comprehensive control over the whole process. The authors later use this methodology in the analysis of high‐dimensional data sets involving microbial populations [FJA*11]. An earlier work that merges visualization and machine learning approaches is by Fuchs et al .…”

Section: Categorization Of Machine Learning Techniques Currently Usedmentioning

confidence: 99%

The State of the Art in Integrating Machine Learning into Visual Analytics

Endert

Ribarsky

Turkay

et al. 2017

Computer Graphics Forum

184

122

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Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-ofthe-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.

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“…Similarly to [52] and [27], Jigsaw allows users to select domains (e.g., people, dates, locations, etc.) to be displayed in the List View.…”

Section: Reduced Parallel Coordinates With Two Domainsmentioning

confidence: 99%

A Five-Level Design Framework for Bicluster Visualizations

Sun

North

Ramakrishnan

2014

IEEE Trans. Visual. Comput. Graphics

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Abstract-Analysts often need to explore and identify coordinated relationships (e.g., four people who visited the same five cities on the same set of days) within some large datasets for sensemaking. Biclusters provide a potential solution to ease this process, because each computed bicluster bundles individual relationships into coordinated sets. By understanding such computed, structural, relations within biclusters, analysts can leverage their domain knowledge and intuition to determine the importance and relevance of the extracted relationships for making hypotheses. However, due to the lack of systematic design guidelines, it is still a challenge to design effective and usable visualizations of biclusters to enhance their perceptablity and interactivity for exploring coordinated relationships. In this paper, we present a five-level design framework for bicluster visualizations, with a survey of the state-of-the-art design considerations and applications that are related or that can be applied to bicluster visualizations. We summarize pros and cons of these design options to support user tasks at each of the five-level relationships. Finally, we discuss future research challenges for bicluster visualizations and their incorporation into visual analytics tools.

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Visual exploration of microbial populations

Cited by 11 publications

References 33 publications

Representative Factor Generation for the Interactive Visual Analysis of High-Dimensional Data

Representative Factor Generation for the Interactive Visual Analysis of High-Dimensional Data

The State of the Art in Integrating Machine Learning into Visual Analytics

A Five-Level Design Framework for Bicluster Visualizations

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