Describing Locations Using Tags and Images: Explorative Pattern Mining in Social Media

  • Florian Lemmerich
  • Martin Atzmueller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7472)

Abstract

This paper presents an approach for explorative pattern mining in social media based on tagging information and collaborative geo-reference annotations. We utilize pattern mining techniques for obtaining sets of tags that are specific for the specified point, landmark, or region of interest. Next, we show how these candidate patterns can be presented and visualized for interactive exploration using a combination of general pattern mining visualizations and views specialized on geo-referenced tagging data. We present a case study using publicly available data from the Flickr photo sharing platform.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wrobel, S.: An Algorithm for Multi-Relational Discovery of Subgroups. In: Komorowski, J., Żytkow, J.M. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  2. 2.
    Atzmueller, M., Lemmerich, F.: Fast Subgroup Discovery for Continuous Target Concepts. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds.) ISMIS 2009. LNCS, vol. 5722, pp. 35–44. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Shneiderman, B.: The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In: Proc. IEEE Symposium on Visual Languages, Boulder, Colorado, pp. 336–343 (1996)Google Scholar
  4. 4.
    Atzmueller, M., Puppe, F.: Semi-Automatic Visual Subgroup Mining using VIKAMINE. Journal of Universal Computer Science (JUCS), Special Issue on Visual Data Mining 11(11), 1752–1765 (2005)Google Scholar
  5. 5.
    Liu, Z.: A Survey on Social Image Mining. In: Chen, R. (ed.) ICICIS 2011 Part I. CCIS, vol. 134, pp. 662–667. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Kennedy, L., Naaman, M.: Generating Diverse and Representative Image Search Results for Landmarks. In: Proceeding of the 17th International Conference on World Wide Web, pp. 297–306. ACM (2008)Google Scholar
  7. 7.
    Lavrac, N., Kavsek, B., Flach, P., Todorovski, L.: Subgroup Discovery with CN2-SD. Journal of Machine Learning Research 5, 153–188 (2004)MathSciNetGoogle Scholar
  8. 8.
    Atzmueller, M., Puppe, F., Buscher, H.P.: Exploiting Background Knowledge for Knowledge-Intensive Subgroup Discovery. In: Proc. 19th Intl. Joint Conf. on Artificial Intelligence (IJCAI 2005), Edinburgh, Scotland, pp. 647–652 (2005)Google Scholar
  9. 9.
    Geng, L., Hamilton, H.J.: Interestingness Measures for Data Mining: A Survey. ACM Computing Surveys 38(3) (2006)Google Scholar
  10. 10.
    Atzmüller, M., Puppe, F.: SD-Map – A Fast Algorithm for Exhaustive Subgroup Discovery. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 6–17. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Lemmerich, F., Rohlfs, M., Atzmueller, M.: Fast discovery of relevant subgroup patterns. In: Proc. 23rd FLAIRS Conference (2010)Google Scholar
  12. 12.
    Reutelshoefer, J., Baumeister, J., Puppe, F.: Towards Meta-Engineering for Semantic Wikis. In: 5th Workshop on Semantic Wikis: Linking Data and People, SemWiki 2010 (2010)Google Scholar
  13. 13.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993–1022 (2003)MATHGoogle Scholar
  14. 14.
    Klösgen, W., Lauer, S.R.W.: 20.1: Visualization of Data Mining Results. In: Handbook of Data Mining and Knowledge Discovery. Oxford University Press, New York (2002)Google Scholar
  15. 15.
    Atzmueller, M., Puppe, F.: A Case-Based Approach for Characterization and Analysis of Subgroup Patterns. Journal of Applied Intelligence 28(3), 210–221 (2008)CrossRefGoogle Scholar
  16. 16.
    Koperski, K., Han, J., Adhikary, J.: Mining Knowledge in Geographical Data. Communications of the ACM 26 (1998)Google Scholar
  17. 17.
    Appice, A., Ceci, M., Lanza, A., Lisi, F., Malerba, D.: Discovery of Spatial Association Rules in Geo-Referenced Census Data: A Relational Mining Approach. Intelligent Data Analysis 7(6), 541–566 (2003)Google Scholar
  18. 18.
    Sigurbjörnsson, B., van Zwol, R.: Flickr Tag Recommendation based on Collective Knowledge. In: Proceeding of the 17th International Conference on World Wide Web, WWW 2008, pp. 327–336. ACM, New York (2008)CrossRefGoogle Scholar
  19. 19.
    Lindstaedt, S., Pammer, V., Mörzinger, R., Kern, R., Mülner, H., Wagner, C.: Recommending Tags for Pictures Based on Text, Visual Content and User Context. In: Proc. 3rd International Conference on Internet and Web Applications and Services, pp. 506–511. IEEE Computer Society, Washington, DC (2008)Google Scholar
  20. 20.
    Abbasi, R., Chernov, S., Nejdl, W., Paiu, R., Staab, S.: Exploiting Flickr Tags and Groups for Finding Landmark Photos. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 654–661. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  21. 21.
    Atzmueller, M., Beer, S., Puppe, F.: Data Mining, Validation and Collaborative Knowledge Capture. In: Brüggemann, S., d’ Amato, C. (eds.) Collaboration and the Semantic Web: Social Networks, Knowledge Networks and Knowledge Resources. IGI Global (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Florian Lemmerich
    • 1
  • Martin Atzmueller
    • 2
  1. 1.Artificial Intelligence and Applied Computer ScienceUniversity of WürzburgWürzburgGermany
  2. 2.Knowledge and Data Engineering GroupUniversity of KasselGermany

Personalised recommendations