Buradasınız

Anasayfa » Content

SOM TİPİNDE YAPAY SİNİR AĞLARINI KULLANARAK TÜRKİYE’NİN İTHALAT YAPTIĞI ÜLKELERİN KÜMELENMESİ ÜZERİNE BİR ÇALIŞMA

By Using SOM Type Neural Network, a Study on Clustering of the Countries Turkey Imports From.

Journal Name:

  • Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi

Publication Year:

  • 2004

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
In this study, the countries Turkey imports from have been clustered by using SOM type neural network. The models established by SOM neural networks have no distribution assumption or correlation assumption for data. Also, this models gives better results when the number of rows and variables in data increase. Moreover, they give the topolojical neigbourhood of the clustes obtained, too. For this reason, SOM neural networks have been preferred to classical statistical clustering methods. As application data, Turkey’s importing data belonging to 2002 year has been used. A software has been writen by using Delphi programming language to organize the raw data and to establish the SOM model. The results obtained have been illustrated by means of tables and figures.
Bookmark/Search this post with
Abstract (Original Language): 
Bu çalışmada, SOM tipinde yapay sinir ağları kullanılarak Türkiye’nin ithalat yaptığı ülkeler kümelenmiştir. SOM sinir ağları ile kurulan modeller, veriler için herhangi bir dağılım ve korelasyon varsayımı içermemektedir. Ayrıca, bu modeller verilerdeki satır sayısı ve değişken sayısı arttıkça daha iyi sonuç vermektedir. Üstelik, bu modeller, elde edilen kümelerin topolojik komşuluğunu da vermektedir. Bu sebeple, klasik istatistiksel kümeleme yöntemleri yerine SOM sinir ağları tercih edilmiştir. Uygulama verisi olarak Türkiye’nin 2002 yılına ait ithalat verileri kullanılmıştır. Bu verilerin düzenlenmesi ve SOM sinir ağı modelinin kurulması için Delphi programlama diliyle bir yazılım geliştirilmiştir. Elde edilen sonuçlar tablo ve grafiklerle gösterilmiştir.
FULL TEXT (PDF): 
PDF icon arastirmax-som-tipinde-yapay-sinir-aglarini-kullanarak-turkiyenin-ithalat-yaptigi-ulkelerin-kumelenmesi-uzerine-bir-calisma.pdf
  • 2
47-68
  • Turkish

REFERENCES

References: 

Cheng, B.and D. M. Titterington (1994), “Neural Networks: A Review from a Statistical
Perspective.”, Statistical Science, Vol. 9, No,1,2-54.
Choudhary, B. and P. Bhattacharyya (2002), “Text Clustering Using Semantics.”, The
Eleventh International World Wide Web Conference.
Creedy, J.and Vance L. Martin (1997), Nonlinear Economic Models, Cheltenham, UK:
Edward Elgar Publishing Ltd .
Fausett, Laurene (1994),Fundamentals of Neural Networks, NJ:Prentice Hall.
Graupe, Daniel (1997), Principles of Artificial Neural Networks, Singapore:World
Scientific Publishing.
Honkela, T., S. Kaski, K. Lagus and T. Kohonen ( 1997), “WEBSOM-Self-Organizing
Maps of Document Collections.”, Proceedings of WSOM'97, Workshop on Self-
Organizing Maps, Espoo, Finland, June 4-6.
Hsu, Arthur L. and S. K. Halgamuge (2003), “Enhancement of Topology Preservation
and Hierchical Dynamic Self- Organizing Maps for Data
Visulisation.”,International Journal of Approximate Reasoning.
Jin, H., Wing-Ho Shum, Kwong-Sak Leung and Man-Leung WONG (2003),
“Expanding Self-Organizing Map For Data Visualization And Cluster
Analysis.”, Information Sciences.
Johnson, R. A. and D. W. Wichern (1988), Applied Multivariate Statistical Analysis,
New Jersey: Prentice Hall.
Kangas, Jari A., T. Kohonen and Jorma T. Laaksonen (1990), “Variants of Self-
Organizing Maps.”, IEEE Transactions on Neural Networks Vol. 1.
Kaski, S. and T. Kohonen (1995), “Exploratory Data Analysis By the Self-Organizing
Map: Structures of Welfare and Poverty in the World.”, Proceedings of the
Third International Conference on Neural Networks in the Capital Markets,
London.
Kay, J. W. and D.M. Titterington (1999), Statistics and Neural Networks, New
York:Oxford University Pres.
Kohonen, Teuvo (2001), Self-Organizing Maps, Berlin:Springer.
Lagus, K., S. Kaski and T. Kohonen (2003), “Mining Massive Document Collections
by the WEBSOM Method.”, Information Sciences.
Lagus, K., T.Honkela, S. Kaski and T. Kohonen (1996), “Self-Organizing Maps of
Document Collections: A New Approach to Interactive Exploration.”,
Proceedings of the Second International Conference on Knowledge Discovery
and Data Mining, California: AAAI Press, Menlo Park, pages 238-243.
Larkin, George Richard (1999), “ Policy Subsystem Portfolio Management:A Neural
Network Model of the Gulf of Mexico Program.” (Yayınlanmamış Doktora
Tezi), Virginia: Faculty of the Virginia Polytechnic Institute and State
University.
Markey, Mia K., Joseph Y. Lo, Georgia D. Tourassi, Carey E. FLOYD (2003), “Self-
Organizing Map for Cluster Analysis of a Breast Cancer Database.”, Artificial
Intelligence in Medicine, Vol. 27, pp. 113–127.
Martinetz, Thomas M., Stanislav G. Berkovich, Klaus J. Schulten (1993), “Neural Gas
Network for Vector Quantization and its Application to Time-Series
Prediction”, IEEE Transactions on Neural Networks, Vol. 4, NO. 4.
Moshou, D., I. Hostens, G. Papaioannou, and Herman RAMON (2000), “Wavelets and
Self-Organizing Maps in Electromyogram (EMG) Analysis.”, European
Symposium on Intelligent Techniques, Aachen, Germany.
Neter, J., W. Wasserman and M. H. Kutner (1989), Applied Linear Regression Models,
Boston:IRWIN.
Öztemel, Ercan (2003), Yapay Sinir Ağları, İstanbul:Papatya yayınevi.
Pal, Nikhil R., James C. Bezdek and Eric C.-K. TSAO (1993), “Generalized Clustering
Networks and Kohonen’s Self Organizing Scheme.”, IEEE Transactions on
Neural Networks, Vol. 4, NO. 4.
Patterson, Dan W (1996), Artificial Neural Networks, New York:Prenctice Hall.
Plaehn, D., T. Fiez and D. Lundahl (2003), “An Advancement in Consumer
Segmentation Using an Unsupervised Self-Organizing Neural Network.”,
“http://www.camo.com/rt/news/infodec/neural”, Web-Seminar.
Rauber, Anderas (1998), “Cluster Visualization in Unsupervised Neural
Networks.”,http://www.ifs.tuwien.ac.at/ifs/research/pub_html/rau_masterth96/th
esis.html
Sarle, W. S. (1994), “Neural Networks and Statistical Models”, Proceedings of the
Nineteeth Annual SAS Users Group International Conference, Cary, NC: SAS
Institute.
Taner, M. Turhan (1997), “ Kohonen’s Self Organizng Networks with Conscience.”,
“www.rocksolidimages.com/pdf/kohonen.pdf”.
Walter, Jorg A. and Klaus J. Schulten (1993), “Implementation of Self-Organizing
Neural Networks for Visuo-Motor Control of an Industrial Robot.”, IEEE
Transactions On Neural Networks, Vol. 4, NO. 1.
Weıjters, T., A. V. Bosch and H. J. Herik (1997), “ Intelligible Neural Networks with
BP-SOM.”, Proceedings of NAIC-97, the Ninth Dutch Conference on Artificial
Intelligence, Helmond, Netherlans:University of Antwerp.
White, Halbert (1989), “Learning in Artificial Neural Networks: A Statistical
Perspective.”, Neural Computation, Vol. 1, No. 4, pp. 425—464.

Thank you for copying data from http://www.arastirmax.com