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Analysis and Interpretation of Visual Hierarchical Heavy Hitters of Binary Relations

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Book cover Advances in Databases and Information Systems (ADBIS 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5207))

Abstract

The emerging field of visual analytics changes the way we model, gather, and analyze data. Current data analysis approaches suggest to gather as much data as possible and then focus on goal and process oriented data analysis techniques. Visual analytics changes this approach and the methodology to interpret the results becomes the key issue.

This paper contributes with a method to interpret visual hierarchical heavy hitters (VHHHs). We show how to analyze data on the general level and how to examine specific areas of the data. We identify five common patterns that build the interpretation alphabet of VHHHs. We demonstrate our method on three different real world datasets and show the effectiveness of our approach.

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References

  1. Adamo, J.-M.: Data mining for association rules and sequential patterns: sequential and parallel algorithms. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  2. Bendix, F., Kosara, R., Hauser, H.: Parallel sets: Visual analysis of categorical data. In: INFOVIS 2005, p. 18. IEEE Computer Society, Los Alamitos (2005)

    Google Scholar 

  3. Chakravarthy, S., Zhang, H.: Visualization of association rules over relational dbmss. In: SAC 2003: Proceedings of the 2003 ACM symposium on Applied computing, pp. 922–926 (2003)

    Google Scholar 

  4. Chintalapani, G., Plaisant, C., Shneiderman, B.: Extending the utility of treemaps with flexible hierarchy. In: IV2004, pp. 335–344 (2004)

    Google Scholar 

  5. Cormode, G., Korn, F., Muthukrishnan, S., Srivastava, D.: Finding hierarchical heavy hitters in data streams. In: VLDB, pp. 464–475 (2003)

    Google Scholar 

  6. Cormode, G., Korn, F., Muthukrishnan, S., Srivastava, D.: Diamond in the rough: finding hierarchical heavy hitters in multi-dimensional data. In: SIGMOD 2004, pp. 155–166 (2004)

    Google Scholar 

  7. Friendly, M.: Visualizing categorical data: Data, stories, and pictures. In: SAS Users Group International 25th Annual Conference (2000)

    Google Scholar 

  8. Hershberger, J., Shrivastava, N., Suri, S., Tòth, C.D.: Space complexity of hierarchical heavy hitters in multi-dimensional data streams. In: PODS2005, pp. 338–347 (2005)

    Google Scholar 

  9. Keim, D.A., Hao, M.C., Dayal, U.: Hierarchical pixel bar charts. IEEE Transactions on Visualization and Computer Graphics 8(3), 255–269 (2002)

    Article  Google Scholar 

  10. Liu, Y., Salvendy, G.: Design and Evaluation of Visualization Support to Facilitate Association Rules Modeling. International Journal of Human-Computer Interaction 21(1), 15–38 (2006)

    Article  Google Scholar 

  11. Mansmann, S., Scholl, M.H.: Exploring olap aggregates with hierarchical visualization techniques. In: SAC 2007, pp. 1067–1073 (2007)

    Google Scholar 

  12. Marakas, G.M.: Modern Data Warehousing, Mining, and Visualization: Core Concepts. Pearson Education, London (2002)

    Google Scholar 

  13. Stolte, C., Tang, D., Hanrahan, P.: Query, analysis, and visualization of hierarchically structured data using polaris. In: SIGKDD, pp. 112–122 (2002)

    Google Scholar 

  14. XVDM System (2007) [Online; accessed 02-October-2007], http://www.inf.unibz.it/dis/wiki/doku.php?id=xvdm:xvdm/

  15. Trivellato, D., Mazeika, A., Böhlen, M.H.: Using 2D hierarchical heavy hitters to investigate binary relationships. In: Simoff, S.J., Böhlen, M.H., Mazeika, A. (eds.) Visual Data Mining. LNCS(LNAI), vol. 4404, pp. 215–236. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  16. Vinnik, S., Mansmann, F.: From analysis to interactive exploration: Building visual hierarchies from olap cubes. In: EDBT, pp. 496–514 (2006)

    Google Scholar 

  17. Wang, J., Miller, D.J., Kesidis, G.: Efficient mining of the multidimensional traffic cluster hierarchy for digesting, visualization, and anomaly identification. Technical Report NAS-TR-0023-2005, Network and Security Research Center, Department of Computer Science and Engineering, Pennsylvania State University, University Park (August 2005)

    Google Scholar 

  18. Wong, P.C., Whitney, P., Thomas, J.: Visualizing association rules for text mining. In: INFOVIS 1999, p. 120 (1999)

    Google Scholar 

  19. Zhang, Y., Singh, S., Sen, S., Duffield, N., Lund, C.: Online identification of hierarchical heavy hitters: algorithms, evaluation, and applications. In: IMC 2004, pp. 101–114 (2004)

    Google Scholar 

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Paolo Atzeni Albertas Caplinskas Hannu Jaakkola

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Mazeika, A., Böhlen, M.H., Trivellato, D. (2008). Analysis and Interpretation of Visual Hierarchical Heavy Hitters of Binary Relations. In: Atzeni, P., Caplinskas, A., Jaakkola, H. (eds) Advances in Databases and Information Systems. ADBIS 2008. Lecture Notes in Computer Science, vol 5207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85713-6_13

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  • DOI: https://doi.org/10.1007/978-3-540-85713-6_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85712-9

  • Online ISBN: 978-3-540-85713-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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