Data Mining Models and Enterprise Risk Management

David L. Olson and Desheng Dash Wu
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David L. Olson: University of Nebraska
Desheng Dash Wu: Stockholm University

Chapter 9 in Enterprise Risk Management Models, 2017, pp 119-132 from Springer

Abstract: 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.

Keywords: Data Mining; Decision Support System; Sentiment Analysis; Decision Tree Model; Correct Classification Rate (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-3-662-53785-5_9

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