Proceedings of the 33rd Annual ACM Symposium on Applied Computing 2018
DOI: 10.1145/3167132.3167203
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Understanding data dimensions by cluster visualization using edge bundling in parallel coordinates

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Cited by 2 publications
(3 citation statements)
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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%
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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%
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