Abstract
The article investigates the usability of conventional data mining tools for handling data sets generated in financial institutions. It discloses the characteristics of “big data” which reveal limitations and new requirements for analytical software to deal with huge data flows related to financial transactions. The performance characteristics of four different conventional data mining tools, their visualization and clustering models are tested for experimental set of big data extracted from bank local data warehouse. The ranking of critical characteristics is provided for each stage of analysis of big data set.
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Tamasauskas, D., Liutvinavicius, M., Sakalauskas, V., Kriksciuniene, D. (2013). Research of Conventional Data Mining Tools for Big Data Handling in Finance Institutions. In: Abramowicz, W. (eds) Business Information Systems Workshops. BIS 2013. Lecture Notes in Business Information Processing, vol 160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41687-3_5
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DOI: https://doi.org/10.1007/978-3-642-41687-3_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41686-6
Online ISBN: 978-3-642-41687-3
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