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Eureka!: A Tool for Interactive Knowledge Discovery
[chapter]
2002
Lecture Notes in Computer Science
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
doi:10.1007/3-540-46146-9_38
fatcat:ubsuhrn7qfghvga4kwbscygzvy