2012
DOI: 10.1111/j.1467-8659.2012.03106.x
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Rolled‐out Wordles: A Heuristic Method for Overlap Removal of 2D Data Representatives

Abstract: When representing 2D data points with spacious objects such as labels, overlap can occur. We present a simple algorithm which modifies the (Mani‐) Wordle idea with scan‐line based techniques to allow a better placement. We give an introduction to common placement techniques from different fields and compare our method to these techniques w.r.t. euclidean displacement, changes in orthogonal ordering as well as shape and size preservation. Especially in dense scenarios our method preserves the overall shape bett… Show more

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Cited by 50 publications
(47 citation statements)
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“…Nevertheless, it is possible to use other ways to calculate the similarity, attaining different results. We use linear RWordle algorithm [47] to remove the overlaps. The topright window presents the initial projection generated by LoCH.…”
Section: Evaluation and Applicationmentioning
confidence: 99%
“…Nevertheless, it is possible to use other ways to calculate the similarity, attaining different results. We use linear RWordle algorithm [47] to remove the overlaps. The topright window presents the initial projection generated by LoCH.…”
Section: Evaluation and Applicationmentioning
confidence: 99%
“…Here, the state of the art is based on systems that exploit the regular, grid-structure of text layout, which does not generalize to arbitrary items. In information visualization, graph drawing [Harel and Koren 2002] and specifically word clouds [Bateman et al 2008;Strobelt et al 2012] share challenges such as collision avoidance with our approach. Placement of textual labels by example was considered by Vollick et al [2007].…”
Section: Previous Workmentioning
confidence: 99%
“…Scott. et al, 2008), Semanticpreserving Word Clouds (Wu et al, 2011), Wordle (Viegas et al, 2009), Rolled-out Wordle (Strobelt et al, 2012), WordTree (Wattenberg and B.Viegas, 2008), or relationships between different terms in a text, such as PhraseNet (van Ham et al, 2009), TextArc (Paley, 2002) and DocBurst (Collins et al, 2009b). The standard Tag Clouds (B.…”
Section: Single Document Visualizationmentioning
confidence: 99%