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  • × theme_ss:"Data Mining"
  1. Wattenberg, M.; Viégas, F.; Johnson, I.: How to use t-SNE effectively (2016) 0.02
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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.
  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. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.02
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    Abstract
    Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
  4. Maaten, L. van den: Learning a parametric embedding by preserving local structure (2009) 0.02
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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.
  5. 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.
  6. Witten, I.H.; Frank, E.: Data Mining : Praktische Werkzeuge und Techniken für das maschinelle Lernen (2000) 0.02
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    Footnote
    Rez. in: nfd 52(2001), H.7, S.427-428 (T. Mandl)
  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. Mandl, T.: Text mining und data minig (2013) 0.01
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  9. Sperlich, T.: ¬Die Zukunft hat schon begonnen : Visualisierungssoftware in der praktischen Anwendung (2000) 0.01
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  10. Brückner, T.; Dambeck, H.: Sortierautomaten : Grundlagen der Textklassifizierung (2003) 0.01
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  11. Baumgartner, R.: Methoden und Werkzeuge zur Webdatenextraktion (2006) 0.01
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    Source
    Semantic Web: Wege zur vernetzten Wissensgesellschaft. Hrsg.: T. Pellegrini, u. A. Blumauer
  12. Huvila, I.: Mining qualitative data on human information behaviour from the Web (2010) 0.01
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    Source
    Information und Wissen: global, sozial und frei? Proceedings des 12. Internationalen Symposiums für Informationswissenschaft (ISI 2011) ; Hildesheim, 9. - 11. März 2011. Hrsg.: J. Griesbaum, T. Mandl u. C. Womser-Hacker
  13. Mandl, T.: Text Mining und Data Mining (2023) 0.01
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  14. Kulathuramaiyer, N.; Maurer, H.: Implications of emerging data mining (2009) 0.01
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    Source
    Social Semantic Web: Web 2.0, was nun? Hrsg.: A. Blumauer u. T. Pellegrini
  15. Song, J.; Huang, Y.; Qi, X.; Li, Y.; Li, F.; Fu, K.; Huang, T.: Discovering hierarchical topic evolution in time-stamped documents (2016) 0.01
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  16. Lowe, D.B.; Dollinger, I.; Koster, T.; Herbert, B.E.: Text mining for type of research classification (2021) 0.01
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  17. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.01
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    Date
    2. 4.2000 18:01:22
  18. Wu, T.; Pottenger, W.M.: ¬A semi-supervised active learning algorithm for information extraction from textual data (2005) 0.01
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  19. Wei, C.-P.; Lee, Y.-H.; Chiang, Y.-S.; Chen, C.-T.; Yang, C.C.C.: Exploiting temporal characteristics of features for effectively discovering event episodes from news corpora (2014) 0.01
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  20. Ekbia, H.; Mattioli, M.; Kouper, I.; Arave, G.; Ghazinejad, A.; Bowman, T.; Suri, V.R.; Tsou, A.; Weingart, S.; Sugimoto, C.R.: Big data, bigger dilemmas : a critical review (2015) 0.01
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