Visual Analysis of Multi‐Dimensional Categorical Data Sets

Broeksema, Bertjan; Telea, Alexandru; Baudel, Thomas

doi:10.1111/cgf.12194

Cited by 32 publications

(64 citation statements)

References 29 publications

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“…The Rule Triggering view shows how production rules map to actual executions. The DataSpace tree and Decision Map build upon the techniques presented in Broeksema et al [6]. These techniques have been initially introduced in the general context of analyzing multivariate categorical data.…”

Section: Relations Between Ontology Concepts and The Decision Logicmentioning

confidence: 99%

“…The MCA visualization in [6] uses a categorical color map to show the identity of the mapped variables. The high number of colors in the view make it hard to interpret.…”

Section: Color Mappingmentioning

confidence: 99%

“…Secondly, we avoid the color mapping problems that occur when color maps variable identity and when the number of variables is high (i.e. 10 or more, as noted in [6]). …”

Section: Color Mappingmentioning

confidence: 99%

See 2 more Smart Citations

Decision Exploration Lab: A Visual Analytics Solution for Decision Management

Broeksema

Baudel

Telea

et al. 2013

IEEE Trans. Visual. Comput. Graphics

Self Cite

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Fig. 1. Decision Exploration Lab in visual querying mode, with the DataSpace tree (A), the Decision Map (B) and the Rule Triggering view (C).Abstract-We present a visual analytics solution designed to address prevalent issues in the area of Operational Decision Management (ODM). In ODM, which has its roots in Artificial Intelligence (Expert Systems) and Management Science, it is increasingly important to align business decisions with business goals. In our work, we consider decision models (executable models of the business domain) as ontologies that describe the business domain, and production rules that describe the business logic of decisions to be made over this ontology. Executing a decision model produces an accumulation of decisions made over time for individual cases. We are interested, first, to get insight in the decision logic and the accumulated facts by themselves. Secondly and more importantly, we want to see how the accumulated facts reveal potential divergences between the reality as captured by the decision model, and the reality as captured by the executed decisions. We illustrate the motivation, added value for visual analytics, and our proposed solution and tooling through a business case from the car insurance industry.

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Section: Relations Between Ontology Concepts and The Decision Logicmentioning

confidence: 99%

“…The MCA visualization in [6] uses a categorical color map to show the identity of the mapped variables. The high number of colors in the view make it hard to interpret.…”

Section: Color Mappingmentioning

confidence: 99%

See 1 more Smart Citation

Decision Exploration Lab: A Visual Analytics Solution for Decision Management

Broeksema

Baudel

Telea

et al. 2013

IEEE Trans. Visual. Comput. Graphics

Self Cite

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show abstract

“…Relevant attributes are visualized as cluster “annotations”. Another example is the work by Broeksema et al [BTB13], which relies on indicator matrices and multiple correspondence analysis to map instances and their attributes to the visual space, resorting to color coded Voronoi partitions to uncover salient attributes from groups of similar instances.…”

Section: Related Workmentioning

confidence: 99%

Uncovering Representative Groups in Multidimensional Projections

Joia

Petronetto

Nonato

2015

Computer Graphics Forum

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Multidimensional projection-based visualization methods typically rely on clustering and attribute selection mechanisms to enable visual analysis of multidimensional data. Clustering is often employed to group similar instances according to their distance in the visual space. However, considering only distances in the visual space may be misleading due to projection errors as well as the lack of guarantees to ensure that distinct clusters contain instances with different content. Identifying clusters made up of a few elements is also an issue for most clustering methods. In this work we propose a novel multidimensional projection-based visualization technique that relies on representative instances to define clusters in the visual space. Representative instances are selected by a deterministic sampling scheme derived from matrix decomposition, which is sensitive to the variability of data while still been able to handle classes with a small number of instances. Moreover, the sampling mechanism can easily be adapted to select relevant attributes from each cluster. Therefore, our methodology unifies sampling, clustering, and feature selection in a simple framework. A comprehensive set of experiments validate our methodology, showing it outperforms most existing sampling and feature selection techniques. A case study shows the effectiveness of the proposed methodology as a visual data analysis tool.

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“…Contributions in visual clustering also emerged in literature from the DR domain. For example, Broeksema et al [21] combine dimensionality reduction and clustering in their tool. The applied clustering is based on a Voronoi parti- tioning of the points in the projection space.…”

Section: Related Workmentioning

confidence: 99%

Cluster Sculptor, an interactive visual clustering system

Bruneau¹,

Pinheiro²,

Broeksema³

et al. 2015

Neurocomputing

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International audienceThis paper describes Cluster Sculptor, a novel interactive clustering system that allows a user to iteratively update the cluster labels of a data set, and an as-sociated low-dimensional projection. The system is fed by clustering results computed in a high-dimensional space, and uses a 2D projection, both as sup-port for overlaying the cluster labels, and engaging user interaction. By easily interacting with elements directly in the visualization, the user can inject his or her domain knowledge progressively, crafting an updated 2D projection and the associated clustering structure that combine his or her preferences and the manifolds underlying the data. Via interactive controls, the distribution of the data in the 2D space can be used to amend the cluster labels, or reciprocally, the 2D projection can be updated so as to emphasize the current clusters. The 2D projection updates follow a smooth physical metaphor, that gives insight of the process to the user. Updates can be interrupted any time, for further data inspection, or modifying the input preferences. The interest of the system is demonstrated by detailed experimental scenarios on three real data sets

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Visual Analysis of Multi‐Dimensional Categorical Data Sets

Cited by 32 publications

References 29 publications

Decision Exploration Lab: A Visual Analytics Solution for Decision Management

Decision Exploration Lab: A Visual Analytics Solution for Decision Management

Uncovering Representative Groups in Multidimensional Projections

Cluster Sculptor, an interactive visual clustering system

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