-
Osinska, V.; Bala, P.: New methods for visualization and improvement of classification schemes : the case of computer science (2010)
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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
-
Christoforidis, A.; Heuwing, B.; Mandl, T.: Visualising topics in document collections : an analysis of the interpretation process of historians (2017)
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- Source
- Everything changes, everything stays the same? - Understanding information spaces : Proceedings of the 15th International Symposium of Information Science (ISI 2017), Berlin/Germany, 13th - 15th March 2017. Eds.: M. Gäde, V. Trkulja u. V. Petras
-
Osinska, V.; Kowalska, M.; Osinski, Z.: ¬The role of visualization in the shaping and exploration of the individual information space : part 1 (2018)
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- Date
- 21.12.2018 17:22:13
-
Wu, K.-C.; Hsieh, T.-Y.: Affective choosing of clustering and categorization representations in e-book interfaces (2016)
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- Date
- 20. 1.2015 18:30:22
-
Wattenberg, M.; Viégas, F.; Johnson, I.: How to use t-SNE effectively (2016)
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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.
-
Maaten, L. van den: Accelerating t-SNE using Tree-Based Algorithms (2014)
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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.
-
Maaten, L. van den: Learning a parametric embedding by preserving local structure (2009)
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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.
-
Maaten, L. van den; Hinton, G.: Visualizing data using t-SNE (2008)
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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.
-
Moya-Anegón, F. de; Vargas-Quesada, B.; Chinchilla-Rodríguez, Z.; Corera-Álvarez, E.; Munoz-Fernández, F.J.; Herrero-Solana, V.; SCImago Group: Visualizing the marrow of science (2007)
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-
Large, A.; Beheshti, J.; Tabatabaei, N.; Nesset, V.: Developing a visual taxonomy : children's views on aesthetics (2009)
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-
Petersen, A.; Münch, V.: STN® AnaVist(TM) holt verborgenes Wissen aus Recherche-Ergebnissen : Neue Software analysiert und visualisiert Marktaufteilung, Forschung und Patentaktivitäten (2005)
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- Abstract
- "Im 21. Jahrhundert ist die entscheidende Herausforderung an Informationsdienstleister nicht, Informationen zugänglich, sondern sie optimal nutzbar zu machen", sagt Sabine Brünger-Weilandt, Geschäftsführerin von FIZ Karlsruhe, das den Online-Dienst STN International in internationaler Kooperation betreibt. Informationsprofis, so Brünger-Weilandt weiter, bräuchten hockentwickelte Software für strategisches Informationsmanagement. Als "Antwort auf diesen Bedarf" hat STN International eine neue Software zur Analyse und Visualisierung (A&V) von Rechercheergebnissen aus STN-Datenbanken entwickelt. STN® AnaVistT(TM) wurde auf der DGI Online-Tagung Ende Mai in Frankfurt am Main und auf Benutzertreffen in Frankfurt am Main, München und Essen vorgestellt. Seit 18. Juli 2005 ist das neue A&V-Werkzeug für die öffentliche Nutzung freigegeben (www.stn-international.de).
-
Maaten, L. van den; Hinton, G.: Visualizing non-metric similarities in multiple maps (2012)
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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.
-
Salaba, A.; Mercun, T.; Aalberg, T.: Complexity of work families and entity-based visualization displays (2018)
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-
Aris, A.; Shneiderman, B.; Qazvinian, V.; Radev, D.: Visual overviews for discovering key papers and influences across research fronts (2009)
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-
Aufreiter, M.: Informationsvisualisierung und Navigation im Semantic Web (2008)
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- Pages
- V, 50 S
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Clavier, V.: Knowledge organization, data and algorithns : the new era of visual representation (2019)
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-
IEEE symposium on information visualization 2003 : Seattle, Washington, October 19 - 21, 2003 ; InfoVis 2003. Proceedings (2003)
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- Editor
- Munzner, T. u. S. North
-
Tscherteu, G.; Langreiter, C.: Explorative Netzwerkanalyse im Living Web (2009)
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- Source
- Social Semantic Web: Web 2.0, was nun? Hrsg.: A. Blumauer u. T. Pellegrini
-
Haller, S.H.M.: Mappingverfahren zur Wissensorganisation (2002)
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Platis, N. et al.: Visualization of uncertainty in tag clouds (2016)
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