Three-Dimensional Bubble Representation and Rule Mining for Evaluating Results of Corporate R & D Personnel’s Innovation Performance

Article Preview

Abstract:

Placing the corporate R & D personnel innovation performance in a three-dimensional space evaluation, which is comprised of innovation capacity, innovation behavior, and innovation performance, we can get final organic combination between all dimensions and the evaluating result of innovation performance, and give its intuitive three-dimensional bubble graphic. Combining with the evaluation index system and the model system, relying on the research data, we make an empirical analysis on R & D personnel's innovation performance, and mine 5 valuable classification rules with rough set method.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 403-408)

Pages:

5160-5165

Citation:

Online since:

November 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] W.D. Jiang. Research On Comprehensive Evaluation Index System of Professional Technicians in Enterprises. Journal of nanjing university (social science edition), Vol. 14(2001, ), p.68–73.

Google Scholar

[2] C.C. Zhu, C.B. Li, L. Zhang. Fuzzy analytic hierarchy process in corporate R&D personnel application performance evaluation. Science and Technology Management Research. Vol. 2(2010), pp.145-147.

Google Scholar

[3] M. Young. The Technical Writer's Handbook. Mill Valley, CA: University Science, (1989).

Google Scholar

[4] W.X. Zhang W.Z. Wu, J.J. Liang etc. Rough sets theory and method. Beijing: science press, (2003).

Google Scholar

[5] J. Gao, J.F. Wang, P. Wei. Indicators for technology innovation performance in firms: situation, problems and New Concepts. Science Research Management, Vol. 25(2004), pp.14-22.

Google Scholar

[6] P.I. Nelson, Shie-Shien Yang. Some properties of Kendall's partial rank correlation coefficient. Statistics & Probability Letters, Vol. 6(1988), pp.147-150.

DOI: 10.1016/0167-7152(88)90110-1

Google Scholar

[7] G. Christian, N. Johanna. Analytical proofs of classical inequalities between Spearman's ρ and Kendall's. Journal of Statistical Planning and Inference, Vol. 139(2009), pp.3795-3798.

DOI: 10.1016/j.jspi.2009.05.017

Google Scholar

[8] L.A. Zadeh. Fuzzy sets and information granularity . Amsterdam: North-Holland Publishing Co., (1979).

Google Scholar