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Exploratory Subgroup Analytics on Ubiquitous Data

Part of the Lecture Notes in Computer Science book series (LNAI,volume 8940)

Abstract

This paper presents exploratory subgroup analytics on ubiquitous data: We propose subgroup discovery and assessment approaches for obtaining interesting descriptive patterns and provide a novel graph-based analysis approach for assessing the relations between the obtained subgroup set. This exploratory visualization approaches allows for the comparison of subgroups according to their relations to other subgroups and to include further parameters, e.g., geo-spatial distribution indicators. We present and discuss analysis results utilizing real-world data given by geo-tagged noise measurements with associated subjective perceptions and a set of tags describing the semantic context.

Keywords

  • Quality Function
  • Pattern Mining
  • Target Concept
  • Semantic Context
  • Subgroup Discovery

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

  1. 1.

    http://vikamine.org.

  2. 2.

    http://rsubgroup.org.

  3. 3.

    http://everyaware.eu.

References

  1. 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)

    CrossRef  Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of VLDB, pp. 487–499. Morgan Kaufmann (1994)

    Google Scholar 

  3. Appice, A., Ceci, M., Lanza, A., Lisi, F., Malerba, D.: Discovery of spatial association rules in geo-referenced census data: a relational mining approach. Intell. Data Anal. 7(6), 541–566 (2003)

    Google Scholar 

  4. Atzmueller, M.: Mining social media: key players, sentiments, and communities. WIREs: Data Min. Knowl. Disc. 2(5), 411–419 (2012)

    Google Scholar 

  5. Atzmueller, M., Becker, M., Doerfel, S., Kibanov, M., Hotho, A., Macek, B.E., Mitzlaff, F., Mueller, J., Scholz, C., Stumme, G.: Ubicon: observing social and physical activities. In: Proceedings of IEEE International Conference on Cyber, Physical and Social Computing, pp. 317–324. IEEE Computer Society, Washington, DC, USA (2012)

    Google Scholar 

  6. Atzmueller, M., Becker, M., Kibanov, M., Scholz, C., Doerfel, S., Hotho, A., Macek, B.E., Mitzlaff, F., Mueller, J., Stumme, G.: Ubicon and its applications for ubiquitous social computing. N. Rev. Hypermedia Multimedia 20(1), 53–77 (2014)

    CrossRef  Google Scholar 

  7. 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)

    CrossRef  Google Scholar 

  8. Atzmueller, M., Lemmerich, F.: VIKAMINE - Open-Source Subgroup Discovery, Pattern Mining, and Analytics. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. LNCS, pp. 842–845. Springer, Berlin (2012)

    CrossRef  Google Scholar 

  9. Atzmueller, M., Lemmerich, F.: Exploratory pattern mining on social media using geo-references and social tagging information. Int. J. Web Sci. (IJWS), 1/2(2) (2013)

    Google Scholar 

  10. Atzmueller, M., Puppe, F.: Semi-automatic visual subgroup mining using VIKAMINE. Journal of Universal Computer Science 11(11), 1752–1765 (2005)

    Google Scholar 

  11. Atzmüller, M., Puppe, F.: A methodological view on knowledge-intensive subgroup discovery. In: Staab, S., Svátek, V. (eds.) EKAW 2006. LNCS (LNAI), vol. 4248, pp. 318–325. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  12. 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)

    CrossRef  Google Scholar 

  13. Atzmueller, M., Puppe, F.: A case-based approach for characterization and analysis of subgroup patterns. J. Appl. Intell. 28(3), 210–221 (2008)

    CrossRef  Google Scholar 

  14. Atzmueller, M., Puppe, F., Buscher, H.P.: Exploiting background knowledge for knowledge-intensive subgroup discovery. In: Proceedings of 19th International Joint Conference on Artificial Intelligence (IJCAI-05), pp. 647–652. Edinburgh, Scotland (2005)

    Google Scholar 

  15. Becker, M., Mueller, J., Hotho, A., Stumme, G.: A generic platform for ubiquitous and subjective data. In: Proceedings of 1st International Workshop on Pervasive Urban Crowdsensing Architecture and Applications, PUCAA 2013 (2013)

    Google Scholar 

  16. Boley, M., Horváth, T., Poigné, A., Wrobel, S.: Listing closed sets of strongly accessible set systems with applications to data mining. Theor. Comput. Sci. 411(3), 691–700 (2010)

    CrossRef  MATH  Google Scholar 

  17. Ceci, M., Appice, A., Malerba, D.: Time-slice density estimation for semantic-based tourist destination suggestion. In: Proceedings of ECAI 2010, pp. 1107–1108. IOS Press, Amsterdam, The Netherlands, The Netherlands (2010)

    Google Scholar 

  18. Diestel, R.: Graph Theory. Springer, Berlin (2006)

    Google Scholar 

  19. Ganter, B., Stumme, G., Wille, R. (eds.): Formal Concept Analysis, Foundations and Applications. Lecture Notes in Computer Science. Springer, Berlin (2005)

    MATH  Google Scholar 

  20. Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Min. Knowl. Disc. 15, 55–86 (2007)

    CrossRef  MathSciNet  Google Scholar 

  21. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)

    Google Scholar 

  22. Hotelling, H.: The generalization of student’s ratio. Ann. Math. Statist. 2(3), 360–378 (1931)

    CrossRef  Google Scholar 

  23. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)

    CrossRef  Google Scholar 

  24. Kleinberg, J.: Bursty and hierarchical structure in streams. In: Proceedings of KDD, pp. 91–101. ACM, New York, NY, USA (2002)

    Google Scholar 

  25. Klösgen, W.: Advances in knowledge discovery and data mining. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Explora: A Multipattern and Multistrategy Discovery Assistant, pp. 249–271. AAAI, California (1996)

    Google Scholar 

  26. Knobbe, A., Fürnkranz, J., Cremilleux, B., Scholz, M.: From local patterns to global models: the lego approach to data mining. In: Proceedings of ECML/PKDD’08 LeGO Workshop (2008)

    Google Scholar 

  27. Koperski, K., Han, J., Adhikary, J.: Mining knowledge in geographical data. Commun. ACM 26, 65–74 (1998)

    Google Scholar 

  28. Lakhal, L., Stumme, G.: Efficient mining of association rules based on formal concept analysis. In: Ganter, B., Stumme, G., Wille, R. (eds.) Formal Concept Analysis. LNCS (LNAI), vol. 3626, pp. 180–195. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  29. van Leeuwen, M., Knobbe, A.J.: Diverse subgroup set discovery. Data Min. Knowl. Discov. 25(2), 208–242 (2012)

    CrossRef  MathSciNet  Google Scholar 

  30. Lemmerich, F., Becker, M., Atzmueller, M.: Generic pattern trees for exhaustive exceptional model mining. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2008, Part II. LNCS, vol. 5212, pp. 277–292. Springer, Heidelberg (2008)

    Google Scholar 

  31. Lemmerich, F., Rohlfs, M., Atzmueller, M.: Fast discovery of relevant subgroup patterns. In: Proceedings of 23rd International FLAIRS Conference, pp. 428–433. AAAI Press, Palo Alto, CA, USA (2010)

    Google Scholar 

  32. 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: Proceedings of 3rd International Conference on Internet and Web Applications and Services, pp. 506–511. IEEE Computer Society, Washington, DC, USA (2008)

    Google Scholar 

  33. Liu, Z.: A survey on social image mining. Intell. Comput. Inf. Sci. 134, 662–667 (2011)

    CrossRef  Google Scholar 

  34. R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2009). http://www.R-project.org

  35. Rattenbury, T., Naaman, M.: Methods for extracting place semantics from flickr tags. ACM Trans. Web 3(1), 1:1–1:30 (2009)

    CrossRef  Google Scholar 

  36. Richter, K.-F., Winter, S.: Citizens as database: conscious ubiquity in data collection. In: Pfoser, D., Tao, Y., Mouratidis, K., Nascimento, M.A., Mokbel, M., Shekhar, S., Huang, Y. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 445–448. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  37. Roitman, H., Raviv, A., Hummel, S., Erera, S., Konopniki, D.: Microcosm: visual discovery, exploration and analysis of social communities. In: Proceedings of IUI, pp. 5–8. ACM, New York, NY, USA (2014)

    Google Scholar 

  38. Santini, S., Ostermaier, B., Adelmann, R.: On the use of sensor nodes and mobile phones for the assessment of noise pollution levels in urban environments. In: Proceedings of International Conference on Networked Sensing Systems (INSS), pp. 1–8 (2009)

    Google Scholar 

  39. Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of IEEE Symposium on Visual Languages, pp. 336–343. Boulder, Colorado (1996)

    Google Scholar 

  40. Sigurbjörnsson, B., van Zwol, R.: Flickr tag recommendation based on collective knowledge. In: Proceeding of the 17th International Conference on World Wide Web, pp. 327–336. WWW ’08, ACM, New York, NY, USA (2008)

    Google Scholar 

  41. Strehl, A., Ghosh, J., Mooney, R.: Impact of similarity measures on web-page clustering. In: AAAI WS AI for Web Search, pp. 58–64. Austin, TX, USA (2000)

    Google Scholar 

  42. Wrobel, S.: An algorithm for multi-relational discovery of subgroups. In: Proceedings of 1st European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD-97), pp. 78–87. Springer, Berlin (1997)

    Google Scholar 

  43. Yin, Z., Cao, L., Han, J., Zhai, C., Huang, T.: Geographical topic discovery and comparison. In: WWW 2011, pp. 247–256. ACM, New York, NY, USA (2011)

    Google Scholar 

  44. Zhang, H., Korayem, M., You, E., Crandall, D.J.: Beyond co-occurrence: discovering and visualizing tag relationships from geo-spatial and temporal similarities. In: Proceedings of International Conference on Web Search and Data Mining, pp. 33–42. ACM, New York, NY, USA (2012)

    Google Scholar 

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Acknowledgements

This work has been supported by the VENUS research cluster at the interdisciplinary Research Center for Information System Design (ITeG) at Kassel University, and parts of this research was funded by the European Union in the 7th Framework programme EveryAware project (FET-Open).

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Atzmueller, M., Mueller, J., Becker, M. (2015). Exploratory Subgroup Analytics on Ubiquitous Data. In: Atzmueller, M., Chin, A., Scholz, C., Trattner, C. (eds) Mining, Modeling, and Recommending 'Things' in Social Media. MUSE MSM 2013 2013. Lecture Notes in Computer Science(), vol 8940. Springer, Cham. https://doi.org/10.1007/978-3-319-14723-9_1

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