Understanding data dimensions by cluster visualization using edge bundling in parallel coordinates

Lima, Rodrigo Santos do Amor Divino; Santos, Carlos Gustavo Resque dos; Mendonça, Sandro de Paula; Morais, Jefferson; Meiguins, Bianchi Serique

doi:10.1145/3167132.3167203

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…Next, we examined the efficiency of our method by comparing its rendering time with both classic PCP and do Amor Divino Lima et al's edge-bundling PCP [8] using the office occupancy detection data set. All methods are implemented in D3.js in Chrome (version: 73.0.3683.103, 64-bit).…”

Section: Resultsmentioning

confidence: 99%

“…Palmas et al [25] present an edge-bundling layout for PCP using density-based clustering for each dimension independently so that the clustering is directly related to the shown dimensions in every part of the plot. Do Amor Divino Lima et al [8] propose an edge-bundling technique to visually encode the clusters information of each dimension, such as variance, means, and quartiles, into the curvature of lines.…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate the effectiveness of our method, we conducted a comparative user study of our method with do Amor Divino Lima et al's edge-bundling PCP [8]. Two typical tasks, correlation estimation and subset estimation were used in the user study, in which accuracy was used as the sole dependent measure.…”

Section: User Studymentioning

confidence: 99%

See 2 more Smart Citations

Confluent-Drawing Parallel Coordinates: Web-Based Interactive Visual Analytics of Large Multi-Dimensional Data

Cui,

Strazdins,

Wang

2019

Preprint

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Parallel coordinates plot is one of the most popular and widely used visualization techniques for multi-dimensional data sets. Its main challenges for large-scale data sets are visual clutter and overplotting which hamper the recognition of patterns and trends in the data. In this paper, we propose a confluent drawing approach of parallel coordinates to support the web-based interactive visual analytics of large multi-dimensional data. The proposed method maps multi-dimensional data to node-link diagrams through the data binning-based clustering for each dimension. It uses density-based confluent drawing to visualize clusters and edges to reduce visual clutter and overplotting. Its rendering time is independent of the number of data items. It supports interactive visualization of large data sets without hardware acceleration in a normal web browser. Moreover, we design interactions to control the data binning process with this approach to support interactive visual analytics of large multi-dimensional data sets. Based on the proposed approach, we implement a web-based visual analytics application. The efficiency of the proposed method is examined through experiments on several data sets. The effectiveness of the proposed method is evaluated through a user study, in which two typical tasks of parallel coordinates plot are performed by participants to compare the proposed method with another parallel coordinates bundling technique. Results show that the proposed method significantly enhances the web-based interactive visual analytics of large multi-dimensional data.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%