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Directed Extended Dependency Analysis for Data Mining
  • Thaddeus T. Shannon, Portland State Universiy
  • Martin Zwick, Portland State University
Document Type
Publication Date
  • Cybernetics,
  • Programming (Computers),
  • Heuristic algorithms,
  • Data mining
Extended dependency analysis (EDA) is a heuristic search technique for finding significant relationships between nominal variables in large data sets. The directed version of EDA searches for maximally predictive sets of independent variables with respect to a target dependent variable. The original implementation of EDA was an extension of reconstructability analysis. Our new implementation adds a variety of statistical significance tests at each decision point that allow the user to tailor the algorithm to a particular objective. It also utilizes data structures appropriate for the sparse data sets customary in contemporary data mining problems. Two examples that illustrate different approaches to assessing model quality tests are given in this paper.

This is the authors' version of a paper which subsequently appeared in Kybnernetes, published by Emerald Group Publishing Limited. The version of record may be found at

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Citation Information
Shannon, T. and Zwick, M. 2004. “Directed Extended Dependency Analysis for Data Mining.” Kybernetes, vol. 33, No. 5/6, pp. 973-983.