This article is devoted to the development of methods of estimation of quality of service of passengers urban passenger transport. For this they key was used tools of statistical analysis.
quality assessment, factor analysis, cluster analysis, sequence re-Grasse
I. ВВЕДЕНИЕ
Кластерный анализ, являясь одним из методов моделирования, также как и другие его разновидности требует проверки адекватности (валидации) своего итогового решения, а именно той структуры (модели), которую он вносит в данные.
1. Gusarova, L. Proverka obosnovannosti klasternogo resheniya / L. Gusarova, I. Yatskiv. Proceedings of the International Conference RELIABILITY and STATISTICS in TRANSPORTATION and COMMUNICATION (RelStat’03). – Riga: Transport and Telecommunication Institute, 2004. – Vol. 2. – P. 49-56.
2. Fedorov, N. Yu. Sovremennoe sostoyanie programmno-apparatnykh kompleksov upravleniya perevozkami legkovym avtotransportom / N. Yu. Fedorov. Modelirovanie sistem i protsessov. – Voronezh. 2010. – №3-4. – S. 45-55.
3. Azuaje, F. A cluster validity framework for genome expression data / F. Azuaje. Bioinformatics. – 2002. – Vol. 18. – P. 319-320.
4. Azuaje, F. Clustering genomic expression data: design and evaluation principles / F. Azuaje, N. Bolshakova. A Practical Approach to Microarray Data Analysis. – 2003. – P. 230-245.
5. Bolshakova, N. Cluster validation techniques for genome expression data / N. Bolshakova, F. Azuaje. Signal Processing. – 2003. – Issue 4. – Vol. 83. – P. 825-833.
6. Dalton, L. Clustering algorithms: on learning, validation, performance, and applications to genomics / L. Dalton, V. Ballarin, M. Brun. Current Genomics. – 2009. –Vol. 10. – № 6. – P. 430-445.
7. Davies, D.L. A cluster separation measure / D. L. Davies, D. W. Bouldin. IEEE Transactions on Pattern Recognition and Machine Intelligence. – 1979. – Issue 1. – Vol. 2. – P. 224-227.
8. Dunn, J. Well separated clusters and optimal fuzzy partitions / J. Dunn. Cybernetics. – 1974. – Vol. 4. – P. 95–104.
9. Lattin, J.M. Analyzing Multivariate Data / J. M. Lattin, J. D. Carroll, P. E. Green. – Belmont, CA: Duxbury Press. – 2003.
10. Rendon, E. Internal versus external cluster validation indexes / E. Rendon, I. Abundez, A. Arizmendi, E. M. Quiroz. International Journal of Computers and Communications. –2011. – Issue 1. – Vol. 5. – P. 27-34.
11. Rousseeuw, P.J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis / P. J. Rousseeuw. Computational and Applied Mathematics. – 1987. – Vol. 20. – P. 53–65.
12. Sarle, W. S. Cubic clustering criterion / W. S. Sarle. SAS Technical Report A-108. – Cary, N.C.: SAS Institute. – 1983. – P. 5