Eureka!: A Tool for Interactive Knowledge Discovery

  • Giuseppe Manco
  • Clara Pizzuti
  • Domenico Talia
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2453)

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.

Keywords

Data Mining Singular Value Decomposition Knowledge Discovery Cluster Result Cluster Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Giuseppe Manco
    • 1
  • Clara Pizzuti
    • 1
  • Domenico Talia
    • 2
  1. 1.ISI-CNRc/o DEIS, Università della CalabriaRende (CS)Italy
  2. 2.DEISUniversità della CalabriaRende (CS)Italy

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