Commenced in January 2007
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Towards Assessment of Indicators Influence on Innovativeness of Countries' Economies: Selected Soft Computing Approaches

Authors: Marta Czyżewska, Krzysztof Pancerz, Jarosław Szkoła

Abstract:

The aim of this paper is to assess the influence of several indicators determining innovativeness of countries' economies by applying selected soft computing methods. Such methods enable us to identify correlations between indicators for period 2006-2010. The main attention in the paper is focused on selecting proper computer tools for solving this problem. As a tool supporting identification, the X-means clustering algorithm, the Apriori rules generation algorithm as well as Self-Organizing Feature Maps (SOMs) have been selected. The paper has rather a rudimentary character. We briefly describe usefulness of the selected approaches and indicate some challenges for further research.

Keywords: Assessment of indicators, innovativeness, soft computing.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1094000

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References:


[1] European Innovation Scoreboard 2011: http://www.proinnoeurope. eu/inno-metrics/page/ius-2011.
[2] L. A. Zadeh, "Fuzzy Logic, Neural Networks, and Soft Computing,” Communication of the ACM, vol. 37, pp. 77–84, 1994.
[3] D. Pelleg and A. W. Moore, "X-means: Extending K-Means with Efficient Estimation of the Number of Clusters,” in Proc. of the Seventeenth International Conference on Machine Learning, P. Langley, Ed., Stanford, CA, USA, 2000, pp. 727–734.
[4] R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules in Large Databases,” in Proc. of the 20th International Conference on Very Large Data Bases, VLDB, Santiago, Chile, 1994, pp. 487–499.
[5] T. Kohonen, "Self-Organized Formation of Topologically Correct Feature Maps,” Biological Cybernetics, vol. 43, no. 1, pp. 59–69, 1982.
[6] K. Cios, W. Pedrycz, R. Swiniarski, and L. Kurgan, Data Mining. A Knowledge Discovery Approach. New York: Springer, 2007.
[7] G. Gan, C. Ma, and J. Wu, Data Clustering. Theory, Algorithms, and Applications, SIAM, Philadelphia, ASA Alexandria, VA, 2007.
[8] I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2005.
[9] M. Czyżewska, J. Szkoła, and K. Pancerz, "Self-Organizing Feature Maps in Correlating Groups of Time Series: Experiments with Indicators Describing Entrepreneurship,” in Proc. of the Workshop on Concurrency, Specification and Programming (CS&P 2012), L. Popova-Zeugmann, Ed., Berlin, Germany, 2012, vol. 1, pp. 73-78.
[10] K. Pancerz, A. Lewicki, and R. Tadeusiewicz, "Ant Based Clustering of Time Series Discrete Data - A Rough Set Approach,” in Swarm, Evolutionary, and Memetic Computing, ser. Lecture Notes in Computer Science, B. K. Panigrahi et al., Eds. Berlin Heidelberg: Springer-Verlag, 2011, vol. 7076, pp. 645–653.