Abstract
In this paper we describe an interactive, visual knowledge discovery tool for analyzing numerical data sets. The tool combines a visual clustering method, to hypothesize meaningful structures in the data, and a classification machine learning algorithm, to validate the hypothesized structures. A two-dimensional representation of the available data allows a user to partition the search space by choosing shape or density according to criteria he deems optimal. A partition can be composed by regions populated according to some arbitrary form, not necessarily spherical. The accuracy of clustering results can be validated by using a decision tree classifier, included in the mining tool.
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Manco, G., Pizzuti, C., Talia, D. (2002). Eureka!: A Tool for Interactive Knowledge Discovery. In: Hameurlain, A., Cicchetti, R., Traunmüller, R. (eds) Database and Expert Systems Applications. DEXA 2002. Lecture Notes in Computer Science, vol 2453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46146-9_38
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DOI: https://doi.org/10.1007/3-540-46146-9_38
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