Skip to main content
SpringerLink
Log in

Visual Analysis and Knowledge Discovery for Text

  • Chapter
  • First Online:
Large-Scale Data Analytics

Abstract

Providing means for effectively accessing and exploring large textual data sets is a problem attracting the attention of text mining and information visualization experts alike. The rapid growth of the data volume and heterogeneity, as well as the richness of metadata and the dynamic nature of text repositories, add to the complexity of the task. This chapter provides an overview of data visualization methods for gaining insight into large, heterogeneous, dynamic textual data sets. We argue that visual analysis, in combination with automatic knowledge discovery methods, provides several advantages. Besides introducing human knowledge and visual pattern recognition into the analytical process, it provides the possibility to improve the performance of automatic methods through user feedback.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://prefuse.org/.

References

  1. Andrews, K., Kienreich, W., Sabol, V., Becker, J., Droschl, G., Kappe, F., Granitzer, M., Auer, P., Tochtermann, K.: The infoSky visual explorer: exploiting hierarchical structure and document similarities. Inf. Vis. 1(3–4), 166–181 (2002)

    Article  Google Scholar 

  2. Bleiholder, J., Naumann, F.: Data fusion. ACM Comput. Surv. 41, 1:1–1:41 (2009)

    Google Scholar 

  3. Bruijn, J.d., Ehrig, M., Feier, C., Martìns-Recuerda, F., Scharffe, F., Weiten, M.: Ontology Mediation, Merging, and Aligning, in Semantic Web Technologies: Trends and Research in Ontology-based Systems (eds J. Davies, R. Studer and P. Warren), John Wiley & Sons, Ltd, Chichester, UK. pp. 95–113. (2006). doi:10.1002/047003033X.ch6

    Google Scholar 

  4. Cao, N., Sun, J., Lin, Y.R., Gotz, D., Liu, S., Qu, H.: Facetatlas: multifaceted visualization for rich text corpora. IEEE Trans. Vis. Comput. Graph. 16(6), 1172–1181 (2010)

    Article  Google Scholar 

  5. Das, D., Martins, A.F.: A survey on automatic text summarization. Technical report, Carnegie Mellon University (2007). Literature Survey for the Language and Statistics II course at CMU

    Google Scholar 

  6. Díaz, J., Petit, J., Serna, M.: A survey of graph layout problems. ACM Comput. Surv. 34, 313–356 (2002)

    Article  Google Scholar 

  7. Dykes, J., MacEachren, A.M., Kraak, M.J. (eds.): Exploring Geovisualization. Elsevier, Amsterdam (2005)

    Google Scholar 

  8. Eppler, M.J., Burkhard, R.A.: Knowledge visualization. In: Schwartz, D. & D. Te’eni (eds.) Encyclopedia of Knowledge Management, Second Edition, PA: Information Science Reference. pp. 987–999. Hershey. doi:10.4018/978-1-59904-931-1.ch094

    Google Scholar 

  9. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17, 37–54 (1996)

    Google Scholar 

  10. Fluit, C.: Autofocus: semantic search for the desktop. Inf. Vis. Int. Conf. 0, 480–487 (2005)

    Google Scholar 

  11. Fodor, I.: A survey of dimension reduction techniques. Technical report UCRL-ID-148494, US DOE Office of Scientific and Technical Information (2002)

    Google Scholar 

  12. Gantz, J.F., Reinsel, D., Chute, C., Schlichting, W., McArthur, J., Minton, S., Xheneti, I., Toncheva, A., Manfrediz, A.: The expanding digital universe, a forecast of worldwide information growth through 2010. IDC White Paper – sponsored by EMC (2007)

    Google Scholar 

  13. Gantz, J.F., Chute, C., Manfrediz, A., Minton, S., Reinsel, D., Schlichting, W., Toncheva, A.: The diverse and exploding digital universe, an updated forecast of worldwide information growth through 2011. IDC White Paper – sponsored by EMC (2008)

    Google Scholar 

  14. Granitzer, M.: Adaptive term weighting through stochastic optimization. In: Gelbukh, A. (ed.) Computational Linguistics and Intelligent Text Processing. Lecture Notes in Computer Science, vol. 6008, pp. 614–626. Springer, Berlin/Heidelberg (2010)

    Chapter  Google Scholar 

  15. Granitzer, M., Neidhart, T., Lux, M.: Learning term spaces based on visual feedback. In: International Workshop on Database and Expert Systems Applications (DEXA), Krakow, pp. 176–180. IEEE Computer Society (2006)

    Google Scholar 

  16. Granitzer, M., Sabol, V., Onn, K.W., Lukose, D., Tochtermann, K.: Ontology alignment – a survey with focus on visually supported semi-automatic techniques. Future Internet 2(3), 238–258 (2010)

    Article  Google Scholar 

  17. Havre, S., Hetzler, E., Whitney, P., Nowell, L.: ThemeRiver: visualizing thematic changes in large document collections. IEEE Trans. Vis. Comput. Graph. 8(1), 9–20 (2002)

    Article  Google Scholar 

  18. Herman, I., Melançon, G., Marshall, M.S.: Graph visualization and navigation in information visualization: A survey. IEEE Trans. Vis. Comput. Graph. 6, 24–43 (2000)

    Article  Google Scholar 

  19. Inselberg, A., Dimsdale, B.: Parallel coordinates for visualizing multi-dimensional geometry. In: CG International ’87 on Computer Graphics 1987. Springer-Verlag New York, Inc., Karuizawa, Japan, New York, NY, USA, pp. 25–44 (1987). http://dl.acm.org/citation.cfm?id=30300.30303

  20. Kaiser, K., Miksch, S.: Information extraction – a survey. Technical report Asgaard-TR-2005-6, Vienna University of Technology (2005)

    Google Scholar 

  21. Kandlhofer, M.: Einbindung neuer Visualisierungskomponenten in ein Multiple Coordinated Views Framework, Endbericht Master-Praktikum (2008)

    Google Scholar 

  22. Kapler, T., Wright, W.: Geo time information visualization. Inf. Vis. 4, 136–146 (2005)

    Article  Google Scholar 

  23. Keim, D.A., Mansmann, F., Oelke, D., Ziegler, H.: Visual analytics: combining automated discovery with interactive visualizations. In: Discovery Science, LNAI, Springer Berlin/ Heidelberg, Budapest, Hungary, pp. 2–14 (2008)

    Google Scholar 

  24. Keim, D.A., Mansmann, F., Schneidewind, J., Thomas, J., Ziegler, H.: Visual analytics: scope and challenges. In: Simoff, S.J., Böhlen, M.H., Mazeika, A. (eds.) Visual Data Mining, pp. 76–90. Springer, Berlin/Heidelberg (2008)

    Chapter  Google Scholar 

  25. Kienreich, W., Seifert, C.: An application of edge bundling techniques to the visualization of media analysis results. In: Proceedings of the International Conference on Information Visualization, London. IEEE Computer Society Press (2010)

    Google Scholar 

  26. Kienreich, W., Zechner, M., Sabol, V.: Comprehensive astronomical visualization for a multimedia encyclopedia. In: International Symposium of Knowledge and Argument Visualization; Proceedings of the International Conference Information Visualisation, Zurich, pp. 363–368. IEEE Computer Society (2007)

    Google Scholar 

  27. Krishnan, M., Bohn, S., Cowley, W., Crow, V., Nieplocha, J.: Scalable visual analytics of massive textual datasets. In: IEEE International Parallel and Distributed Processing Symposium, 2007. IPDPS 2007, Long Beach, pp. 1–10 (2007)

    Google Scholar 

  28. Lex, E., Seifert, C., Kienreich, W., Granitzer, M.: A generic framework for visualizing the news article domain and its application to real-world data. J. Digit. Inf. Manag. 6, 434–441 (2008)

    Google Scholar 

  29. Muhr, M., Kern, R., Granitzer, M.: Analysis of structural relationships for hierarchical cluster labeling. In: Proceedings of the International ACM Conference on Research and Development in Information Retrieval (SIGIR), SIGIR ’10, Geneva, pp. 178–185. ACM, New York (2010)

    Google Scholar 

  30. Muhr, M., Sabol, V., Granitzer, M.: Scalable recursive top-down hierarchical clustering approach with implicit model selection for textual data sets. In: IEEE International Workshop on Text-Based Information Retrieval; Proceedings of the International Conference on Database and Expert Systems Applications, Bilbao (2010)

    Google Scholar 

  31. Müller, F.: Granularity based multiple coordinated views to improve the information seeking process. Ph.D. thesis, University of Konstanz, Germany (2005)

    Google Scholar 

  32. Muthukrishnan, P., Radev, D., Mei, Q.: Edge weight regularization over multiple graphs for similarity learning. In: IEEE 10th International Conference on Data Mining (ICDM), 2010, Sydney, pp. 374–383 (2010). doi:10.1109/ICDM.2010.156

    Google Scholar 

  33. Rennison, E.: Galaxy of news: an approach to visualizing and understanding expansive news landscapes. In: Proceedings of the ACM Symposium on User Interface Software and Technology, UIST ’94, Marina del Rey, pp. 3–12. ACM, New York (1994)

    Google Scholar 

  34. Ribeiro-Neto, B., Baeza-Yates, R.: Modern Information Retrieval: The Concepts and Technology Behind Search, 2nd edn. Pearson Education, Ltd., Harlow, England, Addison-Wesley (2011). http://dblp.uni-trier.de

  35. Risch, J.S., Rex, D.B., Dowson, S.T., Walters, T.B., May, R.A., Moon, B.D.: The STARLIGHT information visualization system. Readings in Information Visualization, pp. 551–560. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  36. Saaty, T.L.: Principia Mathematica Decernendi: Mathematical Principles of Decision Making, 1st edn. RWS Publications, Pittsburgh, PA, USA (2010)

    Google Scholar 

  37. Sabol, V., Kienreich, W., Muhr, M., Klieber, W., Granitzer, M.: Visual knowledge discovery in dynamic enterprise text repositories. In: Proceedings of the International Conference Information Visualisation (IV), pp. 361–368. IEEE Computer Society, Washington, DC (2009)

    Google Scholar 

  38. Sabol, V., Syed, K., Scharl, A., Muhr, M., Hubmann-Haidvogel, A.: Incremental computation of information landscapes for dynamic web interfaces. In: Proceedings of the Brazilian Symposium on Human Factors in Computer Systems, Barcelona, Belo Horizonte, Brazil pp. 205–208 (2010). http://dblp.uni-trier.de/db/conf/ihc/ihc2010.html#SabolSSMH10

  39. Scharl, A., Tochtermann, K.: The Geospatial Web: How Geobrowsers, Social Software and the Web 2.0 are Shaping the Network Society (Advanced Information and Knowledge Processing). Springer, New York/Secaucus (2007)

    Google Scholar 

  40. Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)

    Article  Google Scholar 

  41. Seifert, C., Granitzer, M.: User-based active learning. In: Fan, W., Hsu, W., Webb, G.I., Liu, B., Zhang, C., Gunopulos, D., Wu, X. (eds.) Proceedings of the International Conference on Data Mining Workshops (ICDM), Sydney, pp. 418–425 (2010)

    Google Scholar 

  42. Seifert, C., Lex, E.: A novel visualization approach for data-mining-related classification. In: Proceedings if the International Conference on Information Visualisation (IV), Barcelona, pp. 490–495. Wiley (2009)

    Google Scholar 

  43. Seifert, C., Lex, E.: A visualization to investigate and give feedback to classifiers. In: Proceedings of the European Conference on Visualization (EuroVis), Berlin (2009). Poster

    Google Scholar 

  44. Seifert, C., Kump, B., Kienreich, W., Granitzer, G., Granitzer, M.: On the beauty and usability of tag clouds. In: Proceedings of the International Conference on Information Visualisation (IV), London, pp. 17–25. IEEE Computer Society, Los Alamitos (2008)

    Google Scholar 

  45. Seifert, C., Sabol, V., Granitzer, M.: Classifier hypothesis generation using visual analysis methods. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds.) Networked Digital Technologies. Communications in Computer and Information Science, vol. 87, pp. 98–111. Springer, Berlin/Heidelberg (2010)

    Google Scholar 

  46. Seifert, C., Kienreich, W., Granitzer, M.: Visualizing text classification models with Voronoi word clouds. In: Proceedings of the International Conference Information Visualisation (IV), London (2011). Poster

    Google Scholar 

  47. Shalev-Shwartz, S., Singer, Y., Ng, A.Y.: Online and batch learning of pseudo-metrics. In: International Conference on Machine learning (ICML), Banff, p. 94 (2004)

    Google Scholar 

  48. Shneiderman, B.: Inventing discovery tools: combining information visualization with data mining. Inf. Vis. 1(1), 5–12 (2002)

    Google Scholar 

  49. Shneiderman, B., Plaisant, C.: Designing the User Interface: Strategies for Effective Human-Computer Interaction, 5th edn. Addison-Wesley Publ. Co., Reading, MA, p. 606 (2010)

    Google Scholar 

  50. Thomas, J.J., Cook, K.A. (eds.): Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE Computer Society, Los Alamitos (2005)

    Google Scholar 

  51. Tochtermann, K., Sabol, V., Kienreich, W., Granitzer, M., Becker, J.: Enhancing environmental search engines with information landscapes. In: International Symposium on Environmental Software Systems, Semmering. http://www.isess.org/ (2003)

  52. Tukey, J.W.: Exploratory Data Analysis, 1st edn. Addison Wesley, Massachusetts (1977)

    MATH  Google Scholar 

  53. van Ham, F., Wattenberg, M., Viegas, F.B.: Mapping text with phrase nets. IEEE Trans. Vis. Comput. Graph. 15, 1169–1176 (2009)

    Article  Google Scholar 

  54. Weber, M., Alexa, M., Muller, W.: Visualizing time-series on spirals. In: IEEE Symposium on Information Visualization, 2001. INFOVIS 2001, San Diego, pp. 7–13 (2001)

    Google Scholar 

  55. Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Passau, 94030, Passau, Germany

    Christin Seifert & Michael Granitzer

  2. Know-Center Graz, Inffeldgasse 13/6, A-8010, Graz, Austria

    Vedran Sabol, Wolfgang Kienreich & Elisabeth Lex

Authors
  1. Christin Seifert
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vedran Sabol
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wolfgang Kienreich
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elisabeth Lex
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael Granitzer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christin Seifert .

Editor information

Editors and Affiliations

  1. IBM Research - Ireland, Mulhuddart, Ireland

    Aris Gkoulalas-Divanis

  2. IBM Research - Zurich, Rüschlikon, Switzerland

    Abderrahim Labbi

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Seifert, C., Sabol, V., Kienreich, W., Lex, E., Granitzer, M. (2014). Visual Analysis and Knowledge Discovery for Text. In: Gkoulalas-Divanis, A., Labbi, A. (eds) Large-Scale Data Analytics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-9242-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-9242-9_7

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-9241-2

  • Online ISBN: 978-1-4614-9242-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation