The Projection Explorer: A Flexible Tool for Projection-based Multidimensional Visualization

Paulovich, Fernando V.; Oliveira, Maria Cristina Ferreira de; Minghim, Rosane

doi:10.1109/sibgra.2007.4368165

Cited by 48 publications

(78 citation statements)

References 16 publications

(17 reference statements)

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“…Thus, they are often used simultaneously in exploratory multi-view systems [17]. Few publications tackle the problem of combining both classes into a single approach.…”

Section: Visualization Of High-dimensional Datamentioning

confidence: 99%

Structural Decomposition Trees: Semantic and Practical Implications

Engel

Hagen

Hamann

et al. 2013

Communications in Computer and Information Science

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Abstract. The visualization of high-dimensional data is a challenging research topic. Existing approaches can usually be assigned to either relation or value visualizations. Merging approaches from both classes into a single integrated strategy, Structural Decomposition Trees (SDTs) represent a completely novel visualization approach for high-dimensional data. Although this method is new and promising, statements on how to use and apply the technique in the context of real-world applications are still missing. This paper discusses how SDTs can be interpreted and interacted with to gain insights about the data more effectively. First, it is discussed what properties about the data can be obtained by an interpretation of the initial projection. These statements are also valid for other projections based on principal components analysis, addressing a frequent problem when applying this technique. Further, a detailed and task-oriented interaction guideline shows how provided interaction methods can be utilized effectively for data exploration. The results obtained by an application of these guidelines in air quality research indicate that much insight can be gained even for large and complex data sets. This justifies and further motivates the usefulness and wide applicability of SDTs as a novel visualization approach for high-dimensional data.

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“…Thus, they are often used simultaneously in exploratory multi-view systems [17]. Few publications tackle the problem of combining both classes into a single approach.…”

Section: Visualization Of High-dimensional Datamentioning

confidence: 99%

Structural Decomposition Trees: Semantic and Practical Implications

Engel

Hagen

Hamann

et al. 2013

Communications in Computer and Information Science

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“…Multidimensional projections and point placement techniques have been employed to generate global views of high-dimensional data sets that can be either embedded in metric space, or for which a matrix of pairwise distances may be computed [3,16]. They work by mapping high-dimensional data on a low-dimensional visual space, typically 2D, while striving to lay out similar points close to each other.…”

Section: Introductionmentioning

confidence: 99%

Time-aware visualization of document collections

Alencar

Börner

Paulovich

et al. 2012

Proceedings of the 27th Annual ACM Symposium on Applied Computing

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Scientific articles are the major mechanism for researchers to report their results, and a collection of papers on a discipline can reveal a lot about its evolution, such as the emergence of new topics. Nonetheless, given a broad collection of papers it is typically very difficult to grasp important information that could help readers to globally interpret, navigate and then focus on the relevant items for their task. Content-based document maps are visual representations created from evaluating the (dis)similarity amongst the documents, and have been shown to support exploratory tasks in this scenario. Documents are represented by visual markers placed in the 2D space so that documents close share similar content. Albeit the maps allow visually identifying groups of related documents and frontiers between groups, they do not explicitly convey the temporal evolution of a collection. We propose a technique for creating content-based similarity maps of document collections that highlight temporal changes along time. Our solution constructs a sequence of maps from time-stamped sub-sets of the data. It adopts a cumulative backwards strategy to preserve user context across successive time-stamps, i.e., maps do not change drastically from one time stamp to the next, favouring user perception of changes.

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“…We employ projection techniques to support unsupervised classifications of image data sets aimed at interactive user-directed exploratory analysis. Such techniques have been successfully employed before in Projection Explorer (PEx) [17] to map document sets [16,5] based on their content similarity. We adapt and extend this underlying framework to support user-driven exploration of image data and associated textual information, now called Projection Explorer for Image (PEx-Image).…”

Section: Introductionmentioning

confidence: 99%