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).
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Silhavy, R., Silhavy, P., Prokopova, Z. (2017). Improving Algorithmic Optimisation Method by Spectral Clustering. In: Silhavy, R., Silhavy, P., Prokopova, Z., Senkerik, R., Kominkova Oplatkova, Z. (eds) Software Engineering Trends and Techniques in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 575. Springer, Cham. https://doi.org/10.1007/978-3-319-57141-6_1
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