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Visual Clustering

  • Reference work entry
  • First Online:
Encyclopedia of Database Systems

  • 242 Accesses

Synonyms

Visual data mining; Visual mining

Definition

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. Aggarwal CC. A human-computer interactive method for projected clustering. IEEE Trans Knowl Data Eng. 2004;16(4):448–60.

    Article  Google Scholar 

  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. Asimov D. The grand tour: a tool for viewing multidimensional data. SIAM J Sci Stat Comp. 1985;6(1):128–43.

    Article  MathSciNet  MATH  Google Scholar 

  4. Cook D, Swayne DF. Interactive and dynamic graphics for data analysis – with R and Ggobi. New York: Springer Science and Business Media; 2007.

    Book  MATH  Google Scholar 

  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. 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. Guo D. Coordinating computational and visual approaches for interactive feature selection and multivariate clustering. Inf Vis. 2003;2(4):232–46.

    Article  MathSciNet  Google Scholar 

  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. Hinneburg A, Keim DA, Wawryniuk M. HD-Eye: visual mining of high-dimensional data. IEEE Comput Graph Appl. 1999;19(5):22–31.

    Article  Google Scholar 

  10. Kohonen T. Self-organizing maps. third ed. Berlin: Springer Series in Information Science; 2001.

    Book  MATH  Google Scholar 

  11. Koren Y, Carmel L. Robust linear dimensionality reduction. IEEE Trans Vis Comput Graph. 2004;10(4):459–70.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. Vesanto J. Som-based data visualization methods. Intell Data Anal. 1999;2(3):111–26.

    Article  MATH  Google Scholar 

  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 

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Author information

Authors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Mike Sips

Authors
  1. Mike Sips
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Corresponding author

Correspondence to Mike Sips .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. Computer Science Department, University of Konstanz, Konstanz, Germany

    Daniel A. Keim

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Sips, M. (2018). Visual Clustering. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_1124

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