Chapter

Data Mining

Jacob Furst, Daniela Stan Raicu and Leonard A. Jason

in Handbook of Methodological Approaches to Community-Based Research

Published on behalf of Oxford University Press

Published in print December 2015 | ISBN: 9780190243654
Published online January 2016 | e-ISBN: 9780190461256 | DOI: https://dx.doi.org/10.1093/med:psych/9780190243654.003.0019
Data Mining

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Data mining (also known as artificial intelligence) can uncover patterns and relationships within large samples of people, organizations, or communities that would not otherwise be evident because of the size and complexity of the data. One method of data mining uses decision trees, which attempt to predict a classification (e.g., high-risk neighborhoods in a community), based on successive binary choices. At each branch point of the decision tree, a characteristic is examined (e.g., gang activity within a community), and the decision tree determines whether a characteristic is important in the outcome or classification. In data mining, multiple characteristics are reviewed, and an algorithm is ultimately developed that best predicts class membership (e.g., high- versus low-risk status). The authors illustrates the application of this method to a chronic health condition, showing how computer-generated algorithms were developed to help guide community organizations and government bodies in arriving at more valid and less stigmatizing ways of characterizing patients.

Keywords: Quantitative; Data Mining; Artificial Intelligence; Decision Trees; Algorithm; Chronic Illness; Stigma

Chapter.  6759 words.  Illustrated.

Subjects: Clinical Psychology

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