Perspectives of Innovations, Economics and Business

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Volume 3
Issue 3
Online publication date 2009-12-07
Title Intelligent data analysis - support for development of SMEs sector
Author Grljevic Olivera, Sasa Bosnjak, Zita Bosnjak
Abstract
The paper studies possibilities of intelligent data analysis application for discovering knowledge hidden in small and medium-sized enterprises’ (SMEs) data, on the territory of the province of Vojvodina. The knowledge revealed by intelligent analysis, and not accessible by any other means, could be the valuable starting point for working out  of proactive and preventive actions for the development of the SMEs sector. 
Citation
References
Cahlink, G., 2000. “Data Mining Taps the Trends”, Government Executive Magazine, http://www.govexec.com/tech/articles/ 1000managete ch.html

Cios, L., Kurgan, K., Swiniarski, R., Pedrycz, W., 2007. Data mining:  a knowledge discovery approach, Springer Science Business Media LLC, Heidelberg

Grljević, O., Bošnjak, Z., 2008. “CRISP-DM Methodology utilization in preprocessing small and medium sized enterprises data”, XXXV Symposium on OR, SYM-OP-IS 2008, ISBN: 978-86-7395-248-2, pp.275-279, Belgrade, Serbia

Harrison, P.G., Llado, C.M., 2000. “Performance evaluation of a distributed enterprise data mining system source”, Lecture Notes In Computer Science, Vol.1786, pp.117-31

Smith, K.A., Gupta, J.N.D., 2002. Neural networks in business: techniques and applications, IRM Press, London.
Thearling, K., Becker, B., DeCoste, D., Mawby, B., Pilote, M., Sommerfield, D., 2001. “Visualizing data mining models”, in: Fayyad, U. Et al. (Eds.), Information visualization in data mining and knowledge discovery, Morgan Kaufman

Keywords Intelligent data analysis, CRISP-DM, clustering, small and medium enterprises.
DOI http://dx.doi.org/10.15208/pieb.2009.55
Pages 57-58
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