Search (42 results, page 1 of 3)

  • × theme_ss:"Visualisierung"
  1. Osinska, V.; Bala, P.: New methods for visualization and improvement of classification schemes : the case of computer science (2010) 0.04 
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    Abstract
    Generally, Computer Science (CS) classifications are inconsistent in taxonomy strategies. t is necessary to develop CS taxonomy research to combine its historical perspective, its current knowledge and its predicted future trends - including all breakthroughs in information and communication technology. In this paper we have analyzed the ACM Computing Classification System (CCS) by means of visualization maps. The important achievement of current work is an effective visualization of classified documents from the ACM Digital Library. From the technical point of view, the innovation lies in the parallel use of analysis units: (sub)classes and keywords as well as a spherical 3D information surface. We have compared both the thematic and semantic maps of classified documents and results presented in Table 1. Furthermore, the proposed new method is used for content-related evaluation of the original scheme. Summing up: we improved an original ACM classification in the Computer Science domain by means of visualization.
    Date
    22. 7.2010 19:36:46
  2. Wu, K.-C.; Hsieh, T.-Y.: Affective choosing of clustering and categorization representations in e-book interfaces (2016) 0.03 
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    Date
    20. 1.2015 18:30:22
  3. Wattenberg, M.; Viégas, F.; Johnson, I.: How to use t-SNE effectively (2016) 0.03 
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    Abstract
    Although extremely useful for visualizing high-dimensional data, t-SNE plots can sometimes be mysterious or misleading. By exploring how it behaves in simple cases, we can learn to use it more effectively. We'll walk through a series of simple examples to illustrate what t-SNE diagrams can and cannot show. The t-SNE technique really is useful-but only if you know how to interpret it.
  4. Maaten, L. van den: Accelerating t-SNE using Tree-Based Algorithms (2014) 0.03 
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    Abstract
    The paper investigates the acceleration of t-SNE-an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots-using two tree-based algorithms. In particular, the paper develops variants of the Barnes-Hut algorithm and of the dual-tree algorithm that approximate the gradient used for learning t-SNE embeddings in O(N*logN). Our experiments show that the resulting algorithms substantially accelerate t-SNE, and that they make it possible to learn embeddings of data sets with millions of objects. Somewhat counterintuitively, the Barnes-Hut variant of t-SNE appears to outperform the dual-tree variant.
  5. Maaten, L. van den: Learning a parametric embedding by preserving local structure (2009) 0.03 
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    Abstract
    The paper presents a new unsupervised dimensionality reduction technique, called parametric t-SNE, that learns a parametric mapping between the high-dimensional data space and the low-dimensional latent space. Parametric t-SNE learns the parametric mapping in such a way that the local structure of the data is preserved as well as possible in the latent space. We evaluate the performance of parametric t-SNE in experiments on three datasets, in which we compare it to the performance of two other unsupervised parametric dimensionality reduction techniques. The results of experiments illustrate the strong performance of parametric t-SNE, in particular, in learning settings in which the dimensionality of the latent space is relatively low.
  6. Maaten, L. van den; Hinton, G.: Visualizing data using t-SNE (2008) 0.02 
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    Abstract
    We present a new technique called "t-SNE" that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the data sets.
  7. Maaten, L. van den; Hinton, G.: Visualizing non-metric similarities in multiple maps (2012) 0.02 
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    Abstract
    Techniques for multidimensional scaling visualize objects as points in a low-dimensional metric map. As a result, the visualizations are subject to the fundamental limitations of metric spaces. These limitations prevent multidimensional scaling from faithfully representing non-metric similarity data such as word associations or event co-occurrences. In particular, multidimensional scaling cannot faithfully represent intransitive pairwise similarities in a visualization, and it cannot faithfully visualize "central" objects. In this paper, we present an extension of a recently proposed multidimensional scaling technique called t-SNE. The extension aims to address the problems of traditional multidimensional scaling techniques when these techniques are used to visualize non-metric similarities. The new technique, called multiple maps t-SNE, alleviates these problems by constructing a collection of maps that reveal complementary structure in the similarity data. We apply multiple maps t-SNE to a large data set of word association data and to a data set of NIPS co-authorships, demonstrating its ability to successfully visualize non-metric similarities.
  8. Salaba, A.; Mercun, T.; Aalberg, T.: Complexity of work families and entity-based visualization displays (2018) 0.02 
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  9. IEEE symposium on information visualization 2003 : Seattle, Washington, October 19 - 21, 2003 ; InfoVis 2003. Proceedings (2003) 0.02 
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    Editor
    Munzner, T. u. S. North
  10. Tscherteu, G.; Langreiter, C.: Explorative Netzwerkanalyse im Living Web (2009) 0.02 
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    Source
    Social Semantic Web: Web 2.0, was nun? Hrsg.: A. Blumauer u. T. Pellegrini
  11. Christoforidis, A.; Heuwing, B.; Mandl, T.: Visualising topics in document collections : an analysis of the interpretation process of historians (2017) 0.02 
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  12. Haller, S.H.M.: Mappingverfahren zur Wissensorganisation (2002) 0.02 
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    Date
    30. 5.2010 16:22:35
  13. Platis, N. et al.: Visualization of uncertainty in tag clouds (2016) 0.02 
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    Date
    1. 2.2016 18:25:22
  14. Information visualization : human-centered issues and perspectives (2008) 0.01 
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    Content
    Inhalt: Part I. General Reflections The Value of Information Visualization / Jean-Daniel Fekete, Jarke J van Wijk, John T. Stasko, Chris North Evaluating Information Visualizations / Sheelagh Carpendale Theoretical Foundations of Information Visualization / Helen C. Purchase, Natalia Andrienko, T.J. Jankun-Kelly, Matthew Ward Teaching Information Visualization / Andreas Kerren, John T. Stasko, Jason Dykes Part II. Specific Aspects Creation and Collaboration: Engaging New Audiences for Information Visualization / Jeffrey Heer, Frank van Ham, Sheelagh Carpendale, Chris Weaver, Petra Isenberg Process and Pitfalls in Writing Information Visualization Research Papers / Tamara Munzner Visual Analytics: Definition, Process, and Challenges / Daniel Keim, Gennady Andrienko, Jean-Daniel Fekete, Carsten Görg, Jörn Kohlhammer, Guy Melancon
  15. Mercun, T.; Zumer, M.; Aalberg, T.: Presenting bibliographic families : Designing an FRBR-based prototype using information visualization (2016) 0.01 
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  16. Mercun, T.; Zumer, M.; Aalberg, T.: Presenting bibliographic families using information visualization : evaluation of FRBR-based prototype and hierarchical visualizations (2017) 0.01 
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  17. Brantl, M.; Ceynowa, K.; Meiers, T.; Wolf, T.: Visuelle Suche in historischen Werken (2017) 0.01 
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  18. Burkhard, R.: Knowledge Visualization : Die nächste Herausforderung für Semantic Web Forschende? (2006) 0.01 
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    Source
    Semantic Web: Wege zur vernetzten Wissensgesellschaft. Hrsg.: T. Pellegrini, u. A. Blumauer
  19. Börner, K.: Atlas of knowledge : anyone can map (2015) 0.01 
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    Date
    22. 1.2017 16:54:03
    22. 1.2017 17:10:56
  20. Samoylenko, I.; Chao, T.-C.; Liu, W.-C.; Chen, C.-M.: Visualizing the scientific world and its evolution (2006) 0.01 
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Languages

  • e 33
  • d 8
  • a 1
  • More… Less…

Types

  • a 31
  • el 8
  • m 7
  • x 3
  • s 2
  • More… Less…

Themes

Subjects

Classifications