Using 2D Hierarchical Heavy Hitters to Investigate Binary Relationships

  • Daniel Trivellato
  • Arturas Mazeika
  • Michael H. Böhlen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4404)


This chapter presents VHHH: a visual data mining tool to compute and investigate hierarchical heavy hitters (HHHs) for two-dimensional data. VHHH computes the HHHs for a two-dimensional categorical dataset and a given threshold, and visualizes the HHHs in the three dimensional space. The chapter evaluates VHHH on synthetic and real world data, provides an interpretation alphabet, and identifies common visualization patterns of HHHs.


Visual Data Mining Hierarchical Heavy Hitters Lattice Structure Ordering of Categorical Data 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adamo, J.-M.: Data mining for association rules and sequential patterns: sequential and parallel algorithms. Springer, New York (2001)zbMATHGoogle Scholar
  2. 2.
    Bendix, F., Kosara, R., Hauser, H.: Parallel sets: Visual analysis of categorical data. In: INFOVIS 2005: Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization. IEEE Computer Society, Washington, DC (2005)Google Scholar
  3. 3.
    Beygelzimer, A., Perng, C.-S., Ma, S.: Fast ordering of large categorical datasets for better visualization. In: KDD 2001: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 239–244. ACM Press, New York (2001)CrossRefGoogle Scholar
  4. 4.
    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. ACM Press, New York (2003)CrossRefGoogle Scholar
  5. 5.
    Chintalapani, G., Plaisant, C., Shneiderman, B.: Extending the utility of treemaps with flexible hierarchy. iv 00, 335–344 (2004)Google Scholar
  6. 6.
    Cormode, G., Korn, F., Muthukrishnan, S., Srivastava, D.: Finding hierarchical heavy hitters in data streams. In: VLDB: Proceedings of the 29th Very Large DataBases Conference, Berlin, Germany, pp. 464–475 (2003)Google Scholar
  7. 7.
    Cormode, G., Korn, F., Muthukrishnan, S., Srivastava, D.: Diamond in the rough: finding hierarchical heavy hitters in multi-dimensional data. In: SIGMOD 2004: Proceedings of the 2004 ACM SIGMOD international conference on Management of data, pp. 155–166. ACM Press, New York (2004)CrossRefGoogle Scholar
  8. 8.
    Friendly, M.: Visualizing categorical data: Data, stories, and pictures. In: SAS Users Group International 25th Annual Conference (2000)Google Scholar
  9. 9.
    Hershberger, J., Shrivastava, N., Suri, S., Tòth, C.D.: Space complexity of hierarchical heavy hitters in multi-dimensional data streams. In: PODS 2005: Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pp. 338–347. ACM Press, New York (2005)CrossRefGoogle Scholar
  10. 10.
    Kolatch, E., Weinstein, B.: CatTrees: Dynamic visualization of categorical data using treemaps, Maryland, USA (2001),
  11. 11.
    Liu, Y., Salvendy, G.: Visualization to facilitate association rules modelling: A review. Ergonomia IJEAndHF 27(1), 11–23 (2005)Google Scholar
  12. 12.
    Ma, S., Hellerstein, J.: Ordering categorical data to improve visualization. In: IEEE Symposium on Information Visualization (1999)Google Scholar
  13. 13.
    Marakas, G.M.: Modern Data Warehousing, Mining, and Visualization: Core Concepts. Pearson Education, London (2002)Google Scholar
  14. 14.
    Rosario, G.E., Rundensteiner, E.A., Brown, D.C., Ward, M.O., Huang, S.: Mapping nominal values to numbers for effective visualization. Information Visualization 3(2), 80–95 (2004)CrossRefGoogle Scholar
  15. 15.
    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, PA, USA (August 2005)Google Scholar
  16. 16.
    Wong, P.C., Whitney, P., Thomas, J.: Visualizing association rules for text mining. In: INFOVIS 1999: Proceedings of the 1999 IEEE Symposium on Information Visualization, p. 120. IEEE Computer Society, Washington, DC (1999)CrossRefGoogle Scholar
  17. 17.
    Zhang, Y., Singh, S., Sen, S., Duffield, N., Lund, C.: Online identification of hierarchical heavy hitters: algorithms, evaluation, and applications. In: IMC 2004: Proceedings of the 4th ACM SIGCOMM conference on Internet measurement, pp. 101–114. ACM Press, New York (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Daniel Trivellato
    • 1
  • Arturas Mazeika
    • 1
  • Michael H. Böhlen
    • 1
  1. 1.Faculty of Computer ScienceFree University of Bozen-BolzanoBolzanoItaly

Personalised recommendations