Eureka!: A Tool for Interactive Knowledge Discovery [chapter]

Giuseppe Manco, Clara Pizzuti, Domenico Talia
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
more » ... s 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.
doi:10.1007/3-540-46146-9_38 fatcat:ubsuhrn7qfghvga4kwbscygzvy