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Using Self-Organizing Map for Data Mining: A Synthesis with Accounting Applications

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Data Mining: Foundations and Intelligent Paradigms

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 25))

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Abstract

The self-organizing map (i.e. SOM) has inspired a voluminous body of literature in a number of diverse research domains. We present a synthesis of the pertinent literature as well as demonstrate, via a case study, how SOM can be applied in clustering accounting databases. The synthesis explicates SOM’s theoretical foundations, presents metrics for evaluating its performance, explains the main extensions of SOM, and discusses its main financial applications. The case study illustrates how SOM can identify interesting and meaningful clusters that may exist in accounting databases. The paper extends the relevant literature in that it synthesises and clarifies the salient features of a research area that intersects the domains of SOM, data mining, and accounting.

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Authors and Affiliations

  1. Hanken School of Economics, Arkadiankatu 22, 00101, Helsinki, Finland

    Andriy Andreev & Argyris Argyrou

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  1. Andriy Andreev
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  2. Argyris Argyrou
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Correspondence to Andriy Andreev .

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Editors and Affiliations

  1. College of Letters & Science, Dept. Statistics & Applied Probability, University of California, Santa Barbara, South Hall 5504, Santa Barbara, 93106-3110, California, USA

    Dawn E. Holmes

  2. , School of Electrical and Information Eng, University of South Australia, Adelaide,, Mawson Lakes Campus, SA 5095, Australia

    Lakhmi C Jain

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Andreev, A., Argyrou, A. (2012). Using Self-Organizing Map for Data Mining: A Synthesis with Accounting Applications. In: Holmes, D., Jain, L. (eds) Data Mining: Foundations and Intelligent Paradigms. Intelligent Systems Reference Library, vol 25. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23151-3_14

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  • DOI: https://doi.org/10.1007/978-3-642-23151-3_14

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