Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Visual Clustering

  • Mike SipsEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1124


Visual data mining; Visual mining


Synthesis of computational methods and interactive visualization techniques that represents a clustering structure, defined in higher dimensions to the human analyst in order to support the human analyst to explore and refine the clustering structure of high dimensional data spaces based on his/her domain knowledge.

Historical Background

The advancements made in computing technology over the last two decades allow both scientific and business applications to produce large data sets with increasing complexity and dimensionality. Automated clustering algorithms are indispensable for analyzing large n-dimensional data sets but often fall short to provide completely satisfactory results in terms of quality, meaningfulness, and relevance of the revealed clusters. With the increasing graphics capabilities of the available computers, researchers realized that an integration of the human into the clustering process based on visual feedbacks...

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Recommended Reading

  1. 1.
    Aggarwal CC. A human-computer interactive method for projected clustering. IEEE Trans Knowl Data Eng. 2004;16(4):448–60.CrossRefGoogle Scholar
  2. 2.
    Ankerst M, Breunig MM, Kriegel HP, and Sander J. OPTICS: ordering points to identify the clustering structure. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1999. p. 49–60.Google Scholar
  3. 3.
    Asimov D. The grand tour: a tool for viewing multidimensional data. SIAM J Sci Stat Comp. 1985;6(1):128–43.MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Cook D, Swayne DF. Interactive and dynamic graphics for data analysis – with R and Ggobi. New York: Springer Science and Business Media; 2007.zbMATHCrossRefGoogle Scholar
  5. 5.
    Dhillon IS, Modha DS, Spangler WS. Visualizing class structure of multidimensional data. In: Proceedings of the 30th Symposium on the Interface: Computing Science and Statistics; 1998. p. 488–93.Google Scholar
  6. 6.
    Ester M, Kriegel HP, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining; 1996. p. 226–31.Google Scholar
  7. 7.
    Guo D. Coordinating computational and visual approaches for interactive feature selection and multivariate clustering. Inf Vis. 2003;2(4):232–46.MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hinneburg A, Keim DA. Optimal grid-clustering: towards breaking the curse of dimensionality in high-dimensional clustering. In: Proceedings of the 25th International Conference on Very Large Data Bases; 1999. p. 506–17.Google Scholar
  9. 9.
    Hinneburg A, Keim DA, Wawryniuk M. HD-Eye: visual mining of high-dimensional data. IEEE Comput Graph Appl. 1999;19(5):22–31.CrossRefGoogle Scholar
  10. 10.
    Kohonen T. Self-organizing maps. third ed. Berlin: Springer Series in Information Science; 2001.zbMATHCrossRefGoogle Scholar
  11. 11.
    Koren Y, Carmel L. Robust linear dimensionality reduction. IEEE Trans Vis Comput Graph. 2004;10(4):459–70.CrossRefGoogle Scholar
  12. 12.
    Kraaijveld M, Mao J, Jain A. A nonlinear projection method based on kohonen’s topology preserving maps. IEEE Trans Neural Netw. 1995;6(3):548–59.CrossRefGoogle Scholar
  13. 13.
    Nam EJ, Han Y, Mueller K, Zelenyuk A, Imre D. ClusterSculptor: a visual analytics tool for high-dimensional data. In: IEEE Symposium on Visual Analytics Science and Technology; 2007. p. 75–82.Google Scholar
  14. 14.
    Vesanto J. Som-based data visualization methods. Intell Data Anal. 1999;2(3):111–26.zbMATHCrossRefGoogle Scholar
  15. 15.
    Yang L. Interactive exploration of very large relational datasets through 3D dynamic projections. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2000. p. 236–43.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Stanford UniversityStanfordUSA

Section editors and affiliations

  • Daniel A. Keim
    • 1
  1. 1.Computer Science DepartmentUniversity of KonstanzKonstanzGermany