Abstract
The European Union
paved the way for countries to be divided into sub-units and in a sense paved
the way for the further elaboration of statistical calculations. Level-1 or
Level-2 classifications, which are created not only by the cities but also with
neighboring cities, are of great importance in the development calculations of
countries. Fuzzy Clustering approach comes out as a suitable method if the
clusters are not separated from each other prominently or if some of the
members are indecisive about being a member of the cluster. Fuzzy clusters are
functions that determine each unit between 0 and 1 defined as the membership of
the unit. Units which are very similar take part in the same cluster according
to high membership degree. The purpose here is to determine homogenous city
groups that have the same characteristics in terms of these indicators. In this
study, one of the well known fuzzy clustering methods Gustafson-Kessel is used
for classification SCTU Level-2 regions through development indicators. The
results obtained from the Level-2 classifications were also used to rank the
regions according to their importance. Thus, priority regions can be determined
in investments.
References
- BABAK, R. (2010), “A Cluster Validity İndex for Fuzzy Clustering”, Fuzzy Sets and Systems, 161, 3014–3025.
- BALASKO, B., ABONYI, J. and FEIL, B. (2005), “Fuzzy Clustering and Data Analysis Toolbox”, Univ. of Veszprem, Hungary.
- BEZDEK, J.C. and DUNN, J.C. (1975), “Optimal Fuzzy Partitions: A Heuristic for Estimating the Parameters in a Mixture of Normal Distributions”, IEEE Transactions on Computers, pages 835–838.
- DANİEL, G. and WİTOLD, P. (2007), “Fuzzy C-Means, Gustafson-Kessel FCM, and Kernel-Based FCM: A Comparative Study”, Anal. and Des. of Intel. Sys. using SC Tech., ASC 41, pp. 140–149.
- ERILLI, N.A. (2014), “Bulanık Kümeleme Analizi İle İstatistiki Bölge Birimlerinin (IBBS) Mali Değişkenlere Göre Sınıflandırılması”, Kırıkkale Üniversitesi, Sosyal Bilimler Dergisi, Cilt:4, Sayı:2.
- ERILLI, N.A. (2015), “Socioeconomic Development İndex Ranking Calculations of Cities with Fuzzy Clustering Method: Case of Turkey”, Theoretical and Applied Economics, Volume XXII, No. 1(602), pp. 233-244.
- ERILLI, N.A., YOLCU, U., EĞRIOĞLU, E., ALADAĞ, Ç.H. and ÖNER, Y. (2011), “Determining the Most Proper Number of Cluster in Fuzzy Clustering by Artificial Neural Networks”, Expert Systems with Applications, 38, pp. 2248-2252.
- GATH, I. and GEVA, A.B. (1989), “Unsupervised Optimal Fuzzy Clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 7:773–781.
- GEORGE, G. and HATZİCHRİSTOS, T. (2012), “Comparison of Two Fuzzy Algorithms in Geodemographic Segmentation Analysis: The Fuzzy C-Means and Gustafson-Kessel Methods”, Applied Geography, 34, 125-136.
- GUSTAFSON, D.E. and KESSEL, W.C. (1979), “Fuzzy Clustering with a Fuzzy Covariance Matrix”, IEEE CDC San Diego, 761-766.
- HÖPPNER, F., KLAWONN, F., RUDOLF, K., RUNKLER, T. (1999), “Fuzzy Cluster Analysis”, Wiley&Sons, USA.
- KRİSHNA PRİYA, C.B. and VENKATESWARİ, S. (2017), “ Application of Gustafson-Kessel-like Clustering Algorithm in Delineation of Management Zones in Precision Agriculture”, International Journal of Applied Agricultural Research ISSN 0973-2683 Volume 12, Number 3, pp. 279-293.
- LAİLLY, R. S. and DİAH, S. (2015), “ Analısıs Kelompok Dengan Algorıtma Fuzzy C-Means Dan Gustafson Kessel Clusterıng PADA Indeks LQ45”, Jurnal Gaussıan, Volume 4, Nomor 3, Tahun, Halaman 543-552.
- NAES, T., and MEVIK, T.H. (1999), “The Flexibility of Fuzzy Clustering Illustrated By Examples”, Journal Of Chemo Metrics.
- NUR, A. A., DWİATMONO, A. W. and PRATNYA, P. O. (2016), “ Analisis Clustering Perusahaan Sub Sektor Perbankan Berdasarkan Rasio Keuangan CAMELS Tahun 2014 Menggunakan Metode Fuzzy C-Means dan Fuzzy Gustafson Kessel”, Jurnal Saıns Dan Senı Its Vol. 5, No. 2, 2337-3520.
- OLIVEIRA, J.V., and PEDRYCZ, W. (2007), “Advances In Fuzzy Clustering And Its Applications”, John Wiley &Sons Inc. Pub.,West Sussex, England.
- SHAWKİ A., A. D.and NESAR, A. (2010), “Search Result Clustering Using Fuzzy C-Mean and Gustafon kessel Algorithms: A Comparative Study”, First International Conference on Integrated Intelligent Computing, IEEE Computer Society, 978-0-7695-4152-5/10.
- SOWELL, T. (2010), “Basic Economics: A Common Sense Guide to the Economy”, Basic Books, 4t Ed., USA.
- TAŞKAN, P. (2006), “Classification of Statistical Region Units (İBBS)”, tuikapp.tuik. gov.tr/DIESS/FileUpload/yayinlar/5.iBBS.ppt.
- XİAOHONG, W., JİN, Z., BİN, W., JUN S., CHUNXİA, D. (2018), “Discrimination of Tea Varieties Using FTIR Spectroscopy and Allied Gustafson-Kessel Clustering”, Computers and Electronics in Agriculture, 147, 64–69.
- YOUNG-IL, K., DAE-WON, K., DOHEON, L., KWANG, H.L. (2004), “A Cluster Validation Index for GK Cluster Analysis Based on Relative Degree of Sharing”, Information Sciences, 168, 225–242.
Abstract
Avrupa Birliği, ülkelerin alt birimlere bölünmesinin
yolunu açarak bir anlamda istatistiksel hesaplamaların detaylandırılmasının da
yolunu açmıştır. Sadece şehirlerle değil aynı zamanda şehir komşulukları ile
oluşturulan Düzey-1 ya da Düzey-2 sınıflandırmalarının ülkelerin
gelişmişliklerinin hesaplanmasında büyük bir önemi vardır. Kümelerin biri diğerinden
belirgin bir şekilde ayrılmıyorsa ya da bazı üyeler bir kümenin üyesi olma
konusunda kararsız ise Bulanık Kümeleme yaklaşımının uygun bir yöntem olduğu ortaya
çıkar. Bulanık Kümeler, her bir birimin birim üyeliğini 0-1 aralığında
belirleyen fonksiyonlardır. Yüksek üyelik derecesine göre çok benzer birimler
aynı kümede yer almaktadır. Buradaki amaç, bu göstergeler açısından aynı
özelliklere sahip homojen şehir gruplarını belirlemektir. Bu çalışmada, iyi
bilinen bulanık kümeleme yöntemlerinden biri olan Gustafson-Kessel yöntemi, İİBS
Düzey-2 bölgelerinin gelişim göstergeleri ile sınıflandırılmasında
kullanılmıştır. Düzey-2 sınıflandırmalarından elde edilen sonuçlar, bölgeleri
önem derecelerine göre sınıflandırmak için de kullanılmıştır. Böylece
yatırımlarda öncelikli bölgeler tespit edilebilir.
References
- BABAK, R. (2010), “A Cluster Validity İndex for Fuzzy Clustering”, Fuzzy Sets and Systems, 161, 3014–3025.
- BALASKO, B., ABONYI, J. and FEIL, B. (2005), “Fuzzy Clustering and Data Analysis Toolbox”, Univ. of Veszprem, Hungary.
- BEZDEK, J.C. and DUNN, J.C. (1975), “Optimal Fuzzy Partitions: A Heuristic for Estimating the Parameters in a Mixture of Normal Distributions”, IEEE Transactions on Computers, pages 835–838.
- DANİEL, G. and WİTOLD, P. (2007), “Fuzzy C-Means, Gustafson-Kessel FCM, and Kernel-Based FCM: A Comparative Study”, Anal. and Des. of Intel. Sys. using SC Tech., ASC 41, pp. 140–149.
- ERILLI, N.A. (2014), “Bulanık Kümeleme Analizi İle İstatistiki Bölge Birimlerinin (IBBS) Mali Değişkenlere Göre Sınıflandırılması”, Kırıkkale Üniversitesi, Sosyal Bilimler Dergisi, Cilt:4, Sayı:2.
- ERILLI, N.A. (2015), “Socioeconomic Development İndex Ranking Calculations of Cities with Fuzzy Clustering Method: Case of Turkey”, Theoretical and Applied Economics, Volume XXII, No. 1(602), pp. 233-244.
- ERILLI, N.A., YOLCU, U., EĞRIOĞLU, E., ALADAĞ, Ç.H. and ÖNER, Y. (2011), “Determining the Most Proper Number of Cluster in Fuzzy Clustering by Artificial Neural Networks”, Expert Systems with Applications, 38, pp. 2248-2252.
- GATH, I. and GEVA, A.B. (1989), “Unsupervised Optimal Fuzzy Clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 7:773–781.
- GEORGE, G. and HATZİCHRİSTOS, T. (2012), “Comparison of Two Fuzzy Algorithms in Geodemographic Segmentation Analysis: The Fuzzy C-Means and Gustafson-Kessel Methods”, Applied Geography, 34, 125-136.
- GUSTAFSON, D.E. and KESSEL, W.C. (1979), “Fuzzy Clustering with a Fuzzy Covariance Matrix”, IEEE CDC San Diego, 761-766.
- HÖPPNER, F., KLAWONN, F., RUDOLF, K., RUNKLER, T. (1999), “Fuzzy Cluster Analysis”, Wiley&Sons, USA.
- KRİSHNA PRİYA, C.B. and VENKATESWARİ, S. (2017), “ Application of Gustafson-Kessel-like Clustering Algorithm in Delineation of Management Zones in Precision Agriculture”, International Journal of Applied Agricultural Research ISSN 0973-2683 Volume 12, Number 3, pp. 279-293.
- LAİLLY, R. S. and DİAH, S. (2015), “ Analısıs Kelompok Dengan Algorıtma Fuzzy C-Means Dan Gustafson Kessel Clusterıng PADA Indeks LQ45”, Jurnal Gaussıan, Volume 4, Nomor 3, Tahun, Halaman 543-552.
- NAES, T., and MEVIK, T.H. (1999), “The Flexibility of Fuzzy Clustering Illustrated By Examples”, Journal Of Chemo Metrics.
- NUR, A. A., DWİATMONO, A. W. and PRATNYA, P. O. (2016), “ Analisis Clustering Perusahaan Sub Sektor Perbankan Berdasarkan Rasio Keuangan CAMELS Tahun 2014 Menggunakan Metode Fuzzy C-Means dan Fuzzy Gustafson Kessel”, Jurnal Saıns Dan Senı Its Vol. 5, No. 2, 2337-3520.
- OLIVEIRA, J.V., and PEDRYCZ, W. (2007), “Advances In Fuzzy Clustering And Its Applications”, John Wiley &Sons Inc. Pub.,West Sussex, England.
- SHAWKİ A., A. D.and NESAR, A. (2010), “Search Result Clustering Using Fuzzy C-Mean and Gustafon kessel Algorithms: A Comparative Study”, First International Conference on Integrated Intelligent Computing, IEEE Computer Society, 978-0-7695-4152-5/10.
- SOWELL, T. (2010), “Basic Economics: A Common Sense Guide to the Economy”, Basic Books, 4t Ed., USA.
- TAŞKAN, P. (2006), “Classification of Statistical Region Units (İBBS)”, tuikapp.tuik. gov.tr/DIESS/FileUpload/yayinlar/5.iBBS.ppt.
- XİAOHONG, W., JİN, Z., BİN, W., JUN S., CHUNXİA, D. (2018), “Discrimination of Tea Varieties Using FTIR Spectroscopy and Allied Gustafson-Kessel Clustering”, Computers and Electronics in Agriculture, 147, 64–69.
- YOUNG-IL, K., DAE-WON, K., DOHEON, L., KWANG, H.L. (2004), “A Cluster Validation Index for GK Cluster Analysis Based on Relative Degree of Sharing”, Information Sciences, 168, 225–242.
Details
| Primary Language | English |
|---|---|
| Journal Section | Makaleler |
| Authors | |
| Publication Date | November 30, 2019 |
| Submission Date | September 6, 2019 |
| Published in Issue | Year 2019Volume: 20 Issue: 2 |
Cumhuriyet University Journal of Economics and Administrative Sciences is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY NC).