Abstract
The result of data mining is a set of patterns or models. When presenting these, all or part of the result needs to be explained to the user in order to be understandable and for increasing the user acceptance of the patterns. In doing that, a variety of dimensions for explaining needs to be considered, e.g., from concrete to more abstract explanations. This paper discusses a continuum of explaining for data mining and analysis: It describes how data mining results can be analysed on continuous dimensions and levels. © 2011 Springer-Verlag London Limited.
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CITATION STYLE
Atzmueller, M., & Roth-Berghofer, T. (2011). The mining and analysis continuum of explaining uncovered. In Res. and Dev. in Intelligent Syst. XXVII: Incorporating Applications and Innovations in Intel. Sys. XVIII - AI 2010, 30th SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intel. (pp. 273–278). Springer London. https://doi.org/10.1007/978-0-85729-130-1_20
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