Directed Extended Dependency Analysis for Data Mining

Thaddeus T. Shannon and Martin Zwick

Systems Science Program, Portland State University

Abstract

Extended Dependency Analysis (EDA) is a heuristic search technique for finding significant relationships between nominal variables in large datasets. 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 datasets customary in contemporary data mining problems. Two examples that illustrate different approaches to assessing model quality tests are given.

Discrete Multivariate Modeling Page

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