Towards Assessment of Innovativeness Economy Determinant Correlation: the Double Self-Organizing Feature Map Approach

• Autorzy: Czyżewska M. , Szkoła J. , Pancerz K.

Identyfikatory

DOI

10.3233/FI-2014-959

• Języki publikacji: EN

Abstrakty

EN

In the paper, we attempt to identify the crucial determinants of innovativeness economy and the correlations between the determinants. We based our research on the Innovativeness Union Scoreboard (IUS) dataset. In order to solve the problem, we propose to use the Double Self-Organizing Feature Map (SOM) approach. In the first step, countries, described by determinants of innovativeness economy, are clustered using SOMs according to five year time series for each determinant separately. In the second step, results of the first step are clustered again using SOM to obtain the final correlation represented in the form of a minimal spanning tree. We propose some modifications of the clustering process using SOMs to improve classification results and efficiency of the learning process.

Słowa kluczowe

EN

self-organizing map; technological innovations; correlation; cluster analysis; machine learning; problem solving

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2014
• Tom: Vol. 129, nr 1/2
• Strony: 37--48
• Opis fizyczny: Bibliogr. 12 poz., rys., tab.

Twórcy

autor

Czyżewska M.

• University of Information Technology and Management, Sucharskiego Str. 2, 35-225 Rzeszów, Poland

autor

Szkoła J.

• University of Information Technology and Management, Sucharskiego Str. 2, 35-225 Rzeszów, Poland

autor

Pancerz K.

• University of Information Technology and Management, Sucharskiego Str. 2, 35-225 Rzeszów, Poland

Bibliografia

• [1] Innovation Union Scoreboard 2011: http://www.proinno-europe.eu/inno-metrics/page/ius-2011.
• [2] Amit, R., Glosten, L., Muller, E.: Challenges to theory development in entrepreneurship research, Journal of Management Studies, 30(5), 1993, 815–834.
• [3] Audretsch, D., Ed.: Entrepreneurship, innovation and economic growth, Edward Elgar Publishing Limited, 2006.
• [4] Cios, K., Pedrycz, W., Swiniarski, R., Kurgan, L.: Data mining. A knowledge discovery approach, Springer, New York, 2007.
• [5] Czyżewska,M., Pancerz, K., Szkoła, J.: Self-Organizing Feature Maps in Correlating Groups of Time Series: Experiments with Indicators Describing Entrepreneurship, in: Proceedings of the Workshop on Concurrency, Specification and Programming (CS&P’2012) (L. Popova-Zeugmann, Ed.), vol. 1, Berlin, Germany, 2012, 73–78.
• [6] Czyżewska, M., Pancerz, K., Szkoła, J.: Towards Assessment of Indicators Influence on Innovativeness of Countries’ Economies: Selected Soft Computing Approaches, in: Proceedings of the International Conference on Business Innovation and Technology Management (ICBITM’2012), Dubai, UAE, 2012, 115–118.
• [7] Gan, G., Ma, C., Wu, J.: Data Clustering. Theory, Algorithms, and Applications, SIAM, Philadelphia, ASA Alexandria, VA, 2007.
• [8] Harper, D. A.: Foundations of Entrepreneurship and Economic Development, Routledge Taylor & Francis Group, London and New York, 2003.
• [9] Kohonen, T.: Self-organized formation of topologically correct feature maps, Biological Cybernetics, 43(1), 1982, 59–69.
• [10] Pancerz, K., Lewicki, A., Tadeusiewicz, R.: Ant Based Clustering of Time Series Discrete Data - A Rough Set Approach, in: Swarm, Evolutionary, and Memetic Computing (B. K. Panigrahi, et al., Eds.), vol. 7076 of Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg, 2011, 645–653.
• [11] Pelleg, D.,Moore, A.W.: X-means: ExtendingK-means with Efficient Estimation of the Number of Clusters, Proceedings of the Seventeenth International Conference on Machine Learning (P. Langley, Ed.), Stanford, CA, USA, 2000.
• [12] Witten, I. H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2005.