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Improving Algorithmic Optimisation Method by Spectral Clustering

  • Radek SilhavyEmail author
  • Petr Silhavy
  • Zdenka Prokopova
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 575)

Abstract

In this paper, a spectral algorithm for effort estimation is evaluated. As effort prediction method the Algorithmic Optimisation Method is employed. Spectral clustering is used in version of normalized Laplacian matrix and k-means algorithm is used for clustering eigenvectors. Results shows that clustering lowers a Mean Absolute Percentage Error by 6% and Sum of Squared Errors/Residuals is decreased by 43,5%. Difference in mean value of residuals is statically significant (p = 0.0041, at 0.05 level).

Keywords

Effort estimation Clustering Use case points Algorithmic optimisation method 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic

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