Search (129 results, page 1 of 7)

  • × theme_ss:"Data Mining"
  • × language_ss:"e"
  1. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.03
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    Date
    2. 4.2000 18:01:22
    Source
    Library trends. 48(1999) no.1, S.182-208
  2. KDD : techniques and applications (1998) 0.03
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    Footnote
    A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997
    Source
    Knowledge-based systems. 10(1998) no.7, S.401-470
    Type
    s
  3. Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.02
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    Date
    22.11.1998 18:57:22
    Source
    Online. 21(1997) no.6, S.87-92
  4. Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.02
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    Source
    Information systems. 22(1997) nos.5/6, S.333-347
  5. Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997) 0.02
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    Source
    Information systems. 22(1997) nos.5/6, S.349-385
  6. Ohly, H.P.: Bibliometric mining : added value from document analysis and retrieval (2008) 0.01
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    Pages
    S.181-186
    Source
    Kompatibilität, Medien und Ethik in der Wissensorganisation - Compatibility, Media and Ethics in Knowledge Organization: Proceedings der 10. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation Wien, 3.-5. Juli 2006 - Proceedings of the 10th Conference of the German Section of the International Society of Knowledge Organization Vienna, 3-5 July 2006. Ed.: H.P. Ohly, S. Netscher u. K. Mitgutsch
  7. 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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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.4, S.915-927
  8. Kantardzic, M.: Data mining : concepts, models, methods, and algorithms (2003) 0.01
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    Classification
    PZY (FH K)
    GHBS
    PZY (FH K)
    Pages
    XII, 345 S
  9. Liu, B.: Web data mining : exploring hyperlinks, contents, and usage data (2011) 0.01
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    Classification
    TZG (FH K)
    GHBS
    TZG (FH K)
    Pages
    XX, 622 S
  10. Ma, Z.; Sun, A.; Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter (2013) 0.01
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    Abstract
    Because of Twitter's popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naïve bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1399-1410
  11. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.01
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    Date
    22. 3.2013 19:43:01
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.3, S.574-586
  12. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.01
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
  13. Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.01
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    Date
    7. 3.2019 16:32:22
    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.4, S.402-411
  14. Dang, X.H.; Ong. K.-L.: Knowledge discovery in data streams (2009) 0.01
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  15. Information visualization in data mining and knowledge discovery (2002) 0.00
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    Date
    23. 3.2008 19:10:22
    Footnote
    Rez. in: JASIST 54(2003) no.9, S.905-906 (C.A. Badurek): "Visual approaches for knowledge discovery in very large databases are a prime research need for information scientists focused an extracting meaningful information from the ever growing stores of data from a variety of domains, including business, the geosciences, and satellite and medical imagery. This work presents a summary of research efforts in the fields of data mining, knowledge discovery, and data visualization with the goal of aiding the integration of research approaches and techniques from these major fields. The editors, leading computer scientists from academia and industry, present a collection of 32 papers from contributors who are incorporating visualization and data mining techniques through academic research as well application development in industry and government agencies. Information Visualization focuses upon techniques to enhance the natural abilities of humans to visually understand data, in particular, large-scale data sets. It is primarily concerned with developing interactive graphical representations to enable users to more intuitively make sense of multidimensional data as part of the data exploration process. It includes research from computer science, psychology, human-computer interaction, statistics, and information science. Knowledge Discovery in Databases (KDD) most often refers to the process of mining databases for previously unknown patterns and trends in data. Data mining refers to the particular computational methods or algorithms used in this process. The data mining research field is most related to computational advances in database theory, artificial intelligence and machine learning. This work compiles research summaries from these main research areas in order to provide "a reference work containing the collection of thoughts and ideas of noted researchers from the fields of data mining and data visualization" (p. 8). It addresses these areas in three main sections: the first an data visualization, the second an KDD and model visualization, and the last an using visualization in the knowledge discovery process. The seven chapters of Part One focus upon methodologies and successful techniques from the field of Data Visualization. Hoffman and Grinstein (Chapter 2) give a particularly good overview of the field of data visualization and its potential application to data mining. An introduction to the terminology of data visualization, relation to perceptual and cognitive science, and discussion of the major visualization display techniques are presented. Discussion and illustration explain the usefulness and proper context of such data visualization techniques as scatter plots, 2D and 3D isosurfaces, glyphs, parallel coordinates, and radial coordinate visualizations. Remaining chapters present the need for standardization of visualization methods, discussion of user requirements in the development of tools, and examples of using information visualization in addressing research problems.
    Pages
    xiii, 407 S
    Type
    s
  16. Advances in knowledge discovery and data mining (1996) 0.00
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    Footnote
    Rez. in: JASIS 49(1998) no.4, S.386-387 (F. Exner)
    Pages
    625 S
    Type
    s
  17. Mattison, R.: Data warehousing and data mining for telecommunications (1997) 0.00
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    Footnote
    Rez. in: Database 21(1998) no.3, S.94 (J.D. Blackwell)
    Pages
    273 S
  18. Data mining, data warehousing and client/server databases : Proceedings of the 8th International Hong Kong Computer Society Database Workshop (Academic Stream) (1997) 0.00
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    Pages
    345 S
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  19. Data mining, data warehousing and client/server databases : Proceedings of the 8th International Hong Kong Computer Society Database Workshop (Industrial Stream) (1997) 0.00
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    Pages
    315 S
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  20. Knowledge discovery and data mining (1998) 0.00
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    Source
    Journal of the American Society for Information Science. 49(1998) no.5, S.397-470
    Type
    s

Years

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  • s 14
  • m 13
  • el 5
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