محاسبۀ وجه نقد مورد نیاز شعبه‎ها با استفاده از ‌‌تحلیل چندمتغیرۀ خوشه‌بندی بیزی و پیاده‌سازی آن در شبکه‌های ‌عصبی

بهشتی، ش. (1389). یک مدل برای پیش‎بینی الزامات نقدینگی شعب بانک و پیاده‎سازی آزمایشگاهی با استفاده از شبکه‎های عصبی. پایان‎نامۀ کارشناسی ارشد، تهران، دانشکدۀ مدیریت و حسابداری. دانشگاه علامه طباطبایی.

Beheshti, Sh. (2010). A model to predict the liquidity requirements of bank branches and implemented experimentally using Neural Networks. MSc thesis, Faculty of Management and Accounting, Allameh Tabataba’i University. (in Persian)

Bernardo, J.M & Giron, J. (1988). A Bayesain Approch to cluster Analysis, Questiio, 12(1), 97-112.

Bramer,  M. (2007). Principles of Data Mining. Springer Verlag Berlin Heidelberg.

Blomstedt, P., Tang, J., Xiong, J., Granlund, Ch. & Corander, J. (2015). A Bayesian Predictive Model for Clustering Data of Mixed Discrete and Continuous Type. IEEE transactions on Pattern analysis and machine intelligence, 37(3), 489-498.

Bouveyron, C. & Brunet-Saumard, C. (2014). Model-based clustering of highdimensional data: A review. Computational Statistics and Data Analysis, 71, 52–78.

Cardona, L. & Amaya, M. L. (2012). Cash management cost reduction using data mining to forecast cash demand and LP to optimize resources. Mathematic. Comp.‌, 4 (2), 127-134.

Franzen, J. (2008). Bayesain cluster Analysis. Doctoral dissertation. Department of statistics, Stockholm University.

Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. (1995). Bayesian Data Analysis. London: Chapman and Hall.

Gilks, W. R., Richardson, S. & Spiegelhalter, D. J. (1996). Markov Chain Monte Carlo in Practice. London: Chapman and Hall.

Gower, J. (1971). A general coefficient of similarity and some of its properties. Bio Metrics, 27(4), 857-874.

Han, J. & Kamber, M. (2006). Data Mining: Concepts and Techniques, San Francisco: Morgan Kaufman.

Heard, A.N. , Holmes, Ch.C. & Stephens, A. (2006).‌ A Quantitative Study of Gene Regulation Involved in the Immune Response of Anopheline Mosquitoes: An Application of Bayesian Hierarchical Clustering of Curves. Am erican Statistics Association,‌101(473), 18-29.

Heller, K. A. & Ghahramani, Z. (2005). Bayesian hierarchical clustering. In Twenty-second International Conference on Machine Learning.

Kauffman, L. & Rousseeuw‌, P. (1990). Finding groups in Data: an introduction to cluster analysis, New York: Wiley.

Lin, M.Y. (2013). Bayesian Statistics.Technical Report No. 2, Boston University, Department of health and Management.

Noorbakhsh, I., Heydari, H. & Zavarian, Z. (2010).A diffusion approximation model for vault cash management. 7th Report, Monetary and Banking Research Institute, IRI Central Bank. (in Persian)

Partovi-Nia, V. (2009). Fast High-Dimensional Bayesian Classi_cation and Clustering. Ph.D. thesis, Ecole Polytechnique F_ed_erale de Lasuanne.

Partovi-Nia, V. & Davison, A. C. (2012). High-Dimensional Bayesian Clustering with Variable Selection: The R Package bclust. Journal of Statistical Software, 47(5), 1-22.

Premchand, K. & Walia, E. (2006). Cash Forecasting: An Application of Artificial Neural Networks in Finance, International Journal of Computer Science & Applications‌, 3 (1), 61-77.

Sandipan, R. (2010). ‌Determining Optimal Cash Allocation at ICICI  Bank Branches. Mumbai: ICICI Bank, SAS Global Forum.

Wang‌,‌‌ P. (2008).‌ Clustering and Classification Techniques for Nominal Data Application. Dissertation, Department of Electronic and Engineering, City University of Hong Kong.