An Edge-Bundling Layout for Interactive Parallel Coordinates

Palmas, Gregorio; Bachynskyi, Myroslav; Oulasvirta, Antti; Seidel, Hans Peter; Weinkauf, Tino

doi:10.1109/pacificvis.2014.40

Cited by 65 publications

(37 citation statements)

References 20 publications

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“…Recently, Palmas et al [2014] proposed a method of bundling the edges in the parallel coordinates plots to reduce the clutter. Their technique could also be used in Siren, although the possibility to see a line corresponding to every entity is still important for interaction, as we explain below.…”

Section: Visualizing Redescriptionsmentioning

confidence: 99%

Mining Redescriptions with Siren

Galbrun

Miettinen

2018

ACM Trans. Knowl. Discov. Data

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In many areas of science, scientists need to find distinct common characterizations of the same objects and, vice versa, to identify sets of objects that admit multiple shared descriptions. For example, in biology, an important task is to identify the bioclimatic constraints that allow some species to survive, that is, to describe geographical regions both in terms of the fauna that inhabits them and of their bioclimatic conditions. In data analysis, the task of automatically generating such alternative characterizations is called redescription mining.If a domain expert wants to use redescription mining in his research, merely being able to find redescriptions is not enough. He must also be able to understand the redescriptions found, adjust them to better match his domain knowledge, test alternative hypotheses with them, and guide the mining process towards results he considers interesting. To facilitate these goals, we introduce Siren, an interactive tool for mining and visualizing redescriptions.Siren allows to obtain redescriptions in an anytime fashion through efficient, distributed mining, to examine the results in various linked visualizations, to interact with the results either directly or via the visualizations, and to guide the mining algorithm toward specific redescriptions. In this paper, we explain the features of Siren and why they are useful for redescription mining. We also propose two novel redescription mining algorithms that improve the generalizability of the results compared to the existing ones.

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Section: Visualizing Redescriptionsmentioning

confidence: 99%

Mining Redescriptions with Siren

Galbrun

Miettinen

2018

ACM Trans. Knowl. Discov. Data

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“…As stated by Palmas et al [27], multidimensional data reduction (sampling or clustering) enhances global trends to the cost of potentially concealing pairwise relationships between dimensions. Novotny and Hauser [28] propose a hybrid solution which separates the rendering of outliers from the rest.…”

Section: Overcoming Clutter In Parallel Coordinatesmentioning

confidence: 99%

“…Doing so, in-between axis information is not degraded and clusters are sharper due to outliers having been primarily filtered out. The bundled parallel coordinates presented by Palmas et al [27] uses one-dimensional clustering which improves visual continuity on axes compared to [28]. One-dimensional clustering creates meta-link between axis aggregates, similar to Kosara et al [29]'s Parallel Sets which deals with nominal data inducing natural groups on axes.…”

Section: Overcoming Clutter In Parallel Coordinatesmentioning

confidence: 99%

“…No omnipotent clustering algorithm is suited for all type of data. Various algorithm have been used in state-of-the-art techniques (Fua et al [18] uses Birch algorithm, Van Long and Linsen [26] uses a grid-based algorithm, Palmas et al [27] uses kernel density estimation). In this article, any clustering algorithm can be chosen as long as it produces a strict partitioning of the interval of dimension values, i.e., all resulting clusters should form non-overlapping intervals.…”

Section: Bounding Data Transfermentioning

confidence: 99%

“…Similar to Palmas et al [27], we cluster values on each dimension and bundles lines between pairs of clusters into meta-links. We explored visual metaphors for both types of aggregates (clusters and meta-links) inspired by Palmas et al [27] and Kosara et al [29]'s Parallel Sets while striving to reduce occlusion and retain the general overview. The overall design also resembles Sankey diagrams (e.g., [42]), which are specifically designed to represent flow data.…”

Section: Abstract Parallel Coordinates Designmentioning

confidence: 99%

See 2 more Smart Citations

Visual Exploration of Large Multidimensional Data Using Parallel Coordinates on Big Data Infrastructure

et al. 2017

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Abstract:The increase of data collection in various domains calls for an adaptation of methods of visualization to tackle magnitudes exceeding the number of available pixels on screens and challenging interactivity. This growth of datasets size has been supported by the advent of accessible and scalable storage and computing infrastructure. Similarly, visualization systems need perceptual and interactive scalability. We present a complete system, complying with the constraints of aforesaid environment, for visual exploration of large multidimensional data with parallel coordinates. Perceptual scalability is addressed with data abstraction while interactions rely on server-side data-intensive computation and hardware-accelerated rendering on the client-side. The system employs a hybrid computing method to accommodate pre-computing time or space constraints and achieves responsiveness for main parallel coordinates plot interaction tools on billions of records.

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Geo-Coordinated Parallel Coordinates (GCPC): A Case Study of Environmental Data Analysis

Meseery

Hoeber

2015

Discovery Science

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An Edge-Bundling Layout for Interactive Parallel Coordinates

Cited by 65 publications

References 20 publications

Mining Redescriptions with Siren

Mining Redescriptions with Siren

Visual Exploration of Large Multidimensional Data Using Parallel Coordinates on Big Data Infrastructure

Geo-Coordinated Parallel Coordinates (GCPC): A Case Study of Environmental Data Analysis

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