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Data Mining Models and Enterprise Risk Management

In: Enterprise Risk Management Models

Author

Listed:
  • David L. Olson

    (University of Nebraska)

  • Desheng Dash Wu

    (Stockholm University
    University of Chinese Academy of Sciences)

Abstract

The advent of big data has led to an environment where billions of records are possible. Data mining is demonstrated on a financial risk set of data using R (Rattle) computations for the basic classification algorithms in data mining. We have not demonstrated that scope by any means, but have demonstrated small-scale application of the basic algorithms. The intent is to make data mining less of a black-box exercise, thus hopefully enabling users to be more intelligent in their application of data mining. We demonstrate an open source software product. R is a very useful software, widely used in industry and has all of the benefits of open source software (many eyes are monitoring it, leading to fewer bugs; it is free; it is scalable). Further, the R system enables widespread data manipulation and management.

Suggested Citation

  • David L. Olson & Desheng Dash Wu, 2017. "Data Mining Models and Enterprise Risk Management," Springer Texts in Business and Economics, in: Enterprise Risk Management Models, edition 2, chapter 9, pages 119-132, Springer.
    • Handle: RePEc:spr:sptchp:978-3-662-53785-5_9
    DOI: 10.1007/978-3-662-53785-5_9
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    Cited by:

    1. Evelina Di Corso & Tania Cerquitelli & Daniele Apiletti, 2018. "METATECH: METeorological Data Analysis for Thermal Energy CHaracterization by Means of Self-Learning Transparent Models," Energies, MDPI, vol. 11(6), pages 1-24, May.

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