Puolamäki, K. and Papapetrou, Panagiotis and Lijffijt, J. (2010) Visually controllable data mining methods. In: Webb, G. and Liu, B. and Zhang, C. and Gunopulos, D. and Wu, X. (eds.) International Conference on Data Mining Workshops. Washington, USA: IEEE Computer Society, pp. 409-417. ISBN 9780769542577.
Abstract
A large number of data mining methods are, as such, not applicable to fast, intuitive, and interactive use. Thus, there is a need for visually controllable data mining methods. Such methods should comply with three major requirements: their model structure can be represented visually, they can be controlled using visual, interaction, and they should be fast enough for visual interaction. We, define a framework for using data mining methods in, interactive visualization. These data mining methods are called, ``visually controllable'' and combine data mining with visualization, and user-interaction, bridging the gap between data mining and, visual analytics. Our main objective is to define the interactive, visualization scenario and the requirements for visually, controllable data mining. Basic data mining algorithms are reviewed, and it is demonstrated how they can be controlled visually. We also discuss how existing visual analytics tools fit to the proposed framework., From a data mining perspective, this work creates a reference framework for, designing and evaluating visually controllable algorithms and visual analytics systems.
Metadata
| Item Type: | Book Section |
|---|---|
| Keyword(s) / Subject(s): | data mining, visual analytics, interactive visualization, visually controllable data mining |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Sarah Hall |
| Date Deposited: | 01 Aug 2013 08:31 |
| Last Modified: | 09 Aug 2023 12:34 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/7879 |
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