Point Placement by Phylogenetic Trees and its Application to Visual Analysis of Document Collections

Cuadros, A.M.; Paulovich, Fernando V.; Minghim, Rosane; Telles, Guilherme P.

doi:10.1109/vast.2007.4389002

Cited by 51 publications

(72 citation statements)

References 23 publications

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“…The algorithms to generate similarity layouts [7][8] are inspired on the well-known Neighbor-Joining (NJ) heuristic originally proposed to reconstruct phylogenetic trees. NJ builds unrooted trees, for which the leaf nodes represent the data points and edge lengths indicate dissimilarity.…”

Section: Related Workmentioning

confidence: 99%

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Perception-Based Evaluation of Projection Methods for Multidimensional Data Visualization

Etemadpour

Motta

Paiva

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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Abstract-Similarity-based layouts generated by multidimensional projections or other dimension reduction techniques are commonly used to visualize high-dimensional data. Many projection techniques have been recently proposed addressing different objectives and application domains. Nonetheless, very little is known about the effectiveness of the generated layouts from a user's perspective, how distinct layouts from the same data compare regarding the typical visualization tasks they support, or how domain-specific issues affect the outcome of the techniques. Learning more about projection usage is an important step towards both consolidating their role in high-dimensional data analysis and taking informed decisions when choosing techniques. This work provides a contribution towards this goal. We describe the results of an investigation on the performance of layouts generated by projection techniques as perceived by their users. We conducted a controlled user study to test against the following hypotheses: (1) projection performance is task-dependent; (2) certain projections perform better on certain types of tasks; (3) projection performance depends on the nature of the data; and (4) subjects prefer projections with good segregation capability. We generated layouts of high-dimensional data with five techniques representative of different projection approaches. As application domains we investigated image and document data. We identified eight typical tasks, three of them related to segregation capability of the projection, three related to projection precision, and two related to incurred visual cluttering. Answers to questions were compared for correctness against 'ground truth' computed directly from the data. We also looked at subject confidence and task completion times. Statistical analysis of the collected data resulted in Hypotheses 1 and 3 being confirmed, Hypothesis 2 being confirmed partially and Hypotheses 4 could not be confirmed. We discuss our findings in comparison with some numerical measures of projection layout quality. Our results offer interesting insight on the use of projection layouts in data visualization tasks and provide a departing point for further systematic investigations.

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Section: Related Workmentioning

confidence: 99%

“…We have also included a technique based on Similarity Trees [7], which is a different type of point placement and had not been previously used as a projection. The techniques picked are PCA [3], Isomap [29], LSP [10], Glimmer [30], and NJ tree [8].…”

Section: Projectionsmentioning

confidence: 99%

Perception-Based Evaluation of Projection Methods for Multidimensional Data Visualization

Etemadpour

Motta

Paiva

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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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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“…Significant research has been conducted on improving the sense-making process by providing more convenient visualization techniques [7,13]. Most of these efforts focus on visualizing datasets to more easily reveal the narratives within.…”

Section: Introductionmentioning

confidence: 99%

Capturing and supporting the analysis process

Kadivar

Chen

Dunsmuir

et al. 2009

2009 IEEE Symposium on Visual Analytics Science and Technology

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Visual analytics tools provide powerful visual representations in order to support the sense-making process. In this process, analysts typically iterate through sequences of steps many times, varying parameters each time. Few visual analytics tools support this process well, nor do they provide support for visualizing and understanding the analysis process itself. To help analysts understand, explore, reference, and reuse their analysis process, we present a visual analytics system named CzSaw (See-Saw) that provides an editable and re-playable history navigation channel in addition to multiple visual representations of document collections and the entities within them (in a manner inspired by Jigsaw [24]). Conventional history navigation tools range from basic undo and redo to branching timelines of user actions. In CzSaw's approach to this, first, user interactions are translated into a script language that drives the underlying scripting-driven propagation system. The latter allows analysts to edit analysis steps, and ultimately to program them. Second, on this base, we build both a history view showing progress and alternative paths, and a dependency graph showing the underlying logic of the analysis and dependency relations among the results of each step. These tools result in a visual model of the sense-making process, providing a way for analysts to visualize their analysis process, to reinterpret the problem, explore alternative paths, extract analysis patterns from existing history, and reuse them with other related analyses.

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Point Placement by Phylogenetic Trees and its Application to Visual Analysis of Document Collections

Cited by 51 publications

References 23 publications

Perception-Based Evaluation of Projection Methods for Multidimensional Data Visualization

Perception-Based Evaluation of Projection Methods for Multidimensional Data Visualization

Time-aware visualization of document collections

Capturing and supporting the analysis process

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