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Using self-organizing map for data mining: A synthesis with accounting applications
Hanken School of Economics, Helsinki, Finland.
Hanken School of Economics, Helsinki, Finland.
2012 (English)In: Data mining: Foundations and intelligent paradigms: Volume 3: Medical, health, social, biological and other applications / [ed] Dawn E. Holmes & Lakhmi C. Jain, Berlin: Springer, 2012, p. 321-342Chapter in book (Refereed)
Abstract [en]

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. 

Place, publisher, year, edition, pages
Berlin: Springer, 2012. p. 321-342
Series
Intelligent Systems Reference Library, ISSN 1868-4394 ; 25
National Category
Business Administration Information Systems
Identifiers
URN: urn:nbn:se:hj:diva-39025DOI: 10.1007/978-3-642-23151-3_14Scopus ID: 2-s2.0-84885580042ISBN: 9783642231506 OAI: oai:DiVA.org:hj-39025DiVA, id: diva2:1191798
Available from: 2018-03-20 Created: 2018-03-20 Last updated: 2018-03-20Bibliographically approved

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Argyrou, Argyris

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CiteExportLink to record
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Citation style
  • apa
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Language
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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