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Relational data mining (2001)
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Kulathuramaiyer, N.; Maurer, H.: Implications of emerging data mining (2009)
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Tunbridge, N.: Semiology put to data mining (1999)
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Whittle, M.; Eaglestone, B.; Ford, N.; Gillet, V.J.; Madden, A.: Data mining of search engine logs (2007)
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Analytische Informationssysteme : Data Warehouse, On-Line Analytical Processing, Data Mining (1998)
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- 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
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Goldberg, D.M.; Zaman, N.; Brahma, A.; Aloiso, M.: Are mortgage loan closing delay risks predictable? : A predictive analysis using text mining on discussion threads (2022)
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Lusti, M.: Data Warehousing and Data Mining : Eine Einführung in entscheidungsunterstützende Systeme (1999)
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Analytische Informationssysteme : Data Warehouse, On-Line Analytical Processing, Data Mining (1999)
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Heyer, G.; Läuter, M.; Quasthoff, U.; Wolff, C.: Texttechnologische Anwendungen am Beispiel Text Mining (2000)
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Classification, automation, and new media : Proceedings of the 24th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Passau, March 15 - 17, 2000 (2002)
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Lackes, R.; Tillmanns, C.: Data Mining für die Unternehmenspraxis : Entscheidungshilfen und Fallstudien mit führenden Softwarelösungen (2006)
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Heyer, G.; Quasthoff, U.; Wittig, T.: Text Mining : Wissensrohstoff Text. Konzepte, Algorithmen, Ergebnisse (2006)
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- Editor
- Aggarwal, C.C. u. C.X. Zhai
- Type
- m
-
Methodologies for knowledge discovery and data mining : Third Pacific-Asia Conference, PAKDD'99, Beijing, China, April 26-28, 1999, Proceedings (1999)
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- Editor
- Zhong, N.
-
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.
-
Data mining, data warehousing and client/server databases : Proceedings of the 8th International Hong Kong Computer Society Database Workshop (Industrial Stream) (1997)
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- Editor
- Siu, B., P.K.M. Kwan u. B. Lam
