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Visual Classification

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Encyclopedia of Database Systems

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Synonyms

Cooperative classification

Definition

Decision trees have been successfully used for the task of classification. However, state-of the-art algorithms do not incorporate the user in the tree construction process. Through the involvement of the user in the process of classification, he/she can provide domain knowledge to focus the search of the algorithm and gain a deeper understanding of the resulting decision tree. In a cooperative approach, both the user and the computer contribute what they do best: the user specifies the task, focuses the search using his/her domain knowledge and evaluates the (intermediate) results of the algorithm. The computer, on the other hand, automatically creates patterns satisfying the specified user constraints. The cooperative approach is based on a novel visualization technique for multi-dimensional data representing their impurity with respect to their class labels.

Historical Background

The idea of visual classification has been built upon...

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Recommended Reading

  1. Ankerst M., Elsen C., Ester M., and Kriegel H.-P. Visual classification: an interactive approach to decision tree construction. In Proc. 5th Int. Conf. on Knowledge Discovery and Data Mining, 1999, pp. 392–396.

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  2. Ankerst M., Ester M., and Kriegel H.P. Towards an effective cooperation of the computer and the user for classification. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2000.

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  3. Breiman L., Friedman J.H., Olshen R.A., and Stone P.J. Classification and Reggression Trees. Wadsworth, Belmont, CA, 1984.

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  4. Keim D.A. Visual database exploration techniques. Tutorial at Int. Conf. on Knowledge Discovery and Data Mining, 1997.

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  5. Mehta M., Agrawal R., and Rissanen J. SLIQ: A Fast Scalable Classifier for Data Mining. In Advances in Database Technology, Proc. 5th Int. Conf. on Extending Database Technology, 1996.

    Google Scholar 

  6. Michie D., Spiegelhalter D.J., and Taylor C.C. Machine Learning, Neural and Statistical Classification. Ellis Horwood, 1994. See also http://www.ncc.up.pt/liacc/ML/statlog/datasets.html.

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Authors and Affiliations

    Authors
    1. Mihael Ankerst
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    Editor information

    Editors and Affiliations

    1. College of Computing, Georgia Institute of Technology, 266 Ferst Drive, 30332-0765, Atlanta, GA, USA

      LING LIU (Professor) (Professor)

    2. Database Research Group David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

      M. TAMER ÖZSU (Professor and Director, University Research Chair) (Professor and Director, University Research Chair)

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    © 2009 Springer Science+Business Media, LLC

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    Ankerst, M. (2009). Visual Classification. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_1123

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