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  • × theme_ss:"Data Mining"
  • × type_ss:"el"
  1. Kipcic, O.; Cramer, C.: Wie Zeitungsinhalte Forschung und Entwicklung befördern (2017) 0.02 
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  2. Maaten, L. van den: Accelerating t-SNE using Tree-Based Algorithms (2014) 0.02 
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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.
  3. Lusti, M.: Data Warehousing and Data Mining : Eine Einführung in entscheidungsunterstützende Systeme (1999) 0.01 
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    Date
    17. 7.2002 19:22:06
  4. Jäger, L.: Von Big Data zu Big Brother (2018) 0.01 
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    Date
    22. 1.2018 11:33:49

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