İL NÜFUS VE VATANDAŞLIK MÜDÜRLÜKLERİNİN İŞ YOĞUNLUĞUNA GÖRE HİBRİD KÜMELEME ?LE SINIFLANDIRILMASI

Kamu Kurumları, ilgili görevlerini yerine getirebilmek için değişen nicelikte ve nitelikte personeli istihdam etmelidir. Ancak, istihdam edilen personel sayısının hizmetin verileceği ilin veya bölgenin nüfusu ile her zaman aynı paralellikte olmadığı görülmektedir. Bu durumda, personellerin iradi ya da gayriiradi olarak yer değiştirmesi, geçici nüfus sirkülasyonu ve e-hizmet uygulamasının artışı gibi faktörler etkili olmaktadır. Bu çalışmada, Türkiye'deki İl Nüfus ve Vatandaşlık Müdürlükleri, iş yoğunluklarına göre hibrid hiyerarşik k-ortalamalar kümeleme analizi ile sınıflandırılmıştır. Küme sayısına karar verirken silhouette endeksinden yararlanılmıştır. Analiz sonucunda, benzer iş yoğunluğuna sahip illerden oluşan altı farklı küme yapısı ortaya çıkarılmıştır. Elde edilen küme yapılarının geçerliliği ilgili istatistiki testler yardımıyla da desteklenmiştir

CLASSIFICATION BASED ON WORK INTENSITY BY HYBRID CLUSTERING OFTHE PROVINCIAL DIRECTORATESOF CIVIL REGISTRATION AND NATIONALITY

Public institutions must employ staff in varying number and qualification to fulfill their related responsibilities. However, it is observed that the number of staff are employed and the population of the province or region are not in the same parallels. In this case, some factors are effective: such as the displacement of the staff as voluntary or involuntary, temporary circulation of the population, the increase in electronic applications. In this study, the provincial directorates of civil registration and nationality in Turkey were classified according to their work intensity by hybrid hierarchical k-means clustering. Silhouette index was used to determine the number of clusters. According to the results, it was revealed six different cluster structure consisting of the provinces have a similar work intensity. The validity of the resulting cluster structure was supported with the help of relevant statistical tests

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Alpar, R. (2011) "Uygulamalı Çok Değişkenli İstatistiksel Yöntemler", Ankara, Detay Yayıncılık.
Blashfield, R.K. (1976) "Mixture Model Tests of Cluster Analysis: Accuracy of Four Agglomerative Hierarchical Methods", Psychological Bulletin, 83(3): 377-388.
Bolshakova, N. ve Azuaje, F. (2003) "Cluster Validation Techniques for Genome Expression Data", Signal Processing, 83(4): 825-833.
Cao, F., Liang, J. ve Bai, L. (2009) "A New Initialization Method for Categorical Data Clustering", Expert Systems with Applications, 36(7): 10223-10228.
Chen, B., Tai, P.C., Harrison, R. ve Pan, Y. (2005) "Novel Hybrid Hierarchical-K-means Clustering Method (HK-means) for Microarray Analysis", IEEE Computational Systems Bioinformatics Conference, California-USA.
Chen, B., Rhodes, C., Kline, C. ve Irvin, L. (2010) "Protein Sequence Motif Information Generated by Fuzzy-Hybrid Hierarchical K-means Clustering Algorithm", International Conference on Bioinformatics & Computational Biology (BIOCOMP"10) Conference, Nevada-USA.
Chipman, H. ve Tibshirani, R. (2006) "Hybrid Hierarchical Clustering with Applications to Microarray Data, Biostatistics", 7(2): 286-301.
Everitt, B. S., Landau, S., Leese, M. Ve Stahl, D. (2001) "Hierarchical Clustering in Cluster Analysis", John Wiley & Sons, 5th Edition, Kings College, London, UK.
Ferreira, L. ve Hitchcock, D.B. (2009) "A Comparison of Hierarchical Methods for Clustering Functional Data", Comminications in Statistics - Simulation & Computation, 38(9): 1925-1949.
Fraley, C. ve Raftery, A.E. (1998) "How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis", The Computer Journal, 41(8): 578-588.
Gan, G., Ma, C. ve Wu, J. (2007) "Data Clustering Theory, Algorithms and Applications", Philadelphia, Asa-Siam Series.
Hands, S, Everitt, B. (1987) "A Monte Carlo Study of the Recovery of Cluster Structure in Binary Data by Hierarchical Clustering Techniques", Multivariate Behavioral Res., 22(2): 235-243.
Hubert, L. (1974) "Approximate Evaluation Techniques for the Single-Link and Complete-Link Hierarchical Clustering Procedures", Journal of the American Statistical Association, 69(347): 698-704.
Kuiper, F.K. ve Fisher, L. (1975) "A Monte Carlo Comparison of Six Clustering Procedures", Biometrics, 31(3): 777-783.
MacQueen, J. (1967) "Some Methods for Classification and Analysis of Multivariate Observations", In Proceedings of The Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1: 281-297.
Milligan, G. W. (1980) "An Exemination of the Effect of Six Types of Error Perturbation on Fifteen Clustering Algorithms", Psychometrika, 45(3): 325-342.
Milligan, G.W. ve Cooper, M.C. (1988) "A Study of Standardization of Variables in Cluster Analysis", 5(2): 181-204.
Murugesan, K. ve Zhang, C. (2011) "Hybrid Bisect K-means Clustering Algorithm", Business Computing and Global Informatization International Conference, Shanghai-China.
Ng, R. T. ve Han, J. (1994) "Efficient and Effective Clustering Methods for Spatial Data Mining", 20th International Conference on Very Large Data Bases, Santiago de Chile-Chile.
Özdamar, K. (1999) "Paket Programlar ile istatistiksel Veri Analizi 2", Eskişehir: Kaan Kitabevi.
Rao, A.R. ve Srinivas, V.V. (2008) "Regionalization of Watersheds: An Approach Based on Cluster Analysis", Water Science and Technology Library, Vol. 58, Springer, USA.
Rendon, E., Abundez, I., Arizmendi, A. ve Quiroz, M. (2011) "Internal versus External Cluster Validation Indexes", International Journal of Computers and Communications, 5(1): 27-34.
Rousseeuw, P. J. (1987) "Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis", Journal of Computational and Applied Mathematics, 20: 53-65.
Singh, N. ve Singh, D. (2012) "Performance Evaluation of K-Means and Heirarichal Clustering in Terms of Accuracy and Running Time", (IJCSIT) International Journal of Computer Science and Information Technologies, 3(3): 4119-4121.
Syal, R., Prasad, G.V.S.R. ve Kumar, V.V. (2012) "A Novel Hybrid Clustering Algorithm: Integrated Partitional and Hierarchical Clustering Algorithm for Categorical Data", International Journal Computer Science & Emerging Technologies, 3(5): 138-146.
Templ, M., Filzmoser, P. ve Reimann, C. (2008) "Cluster Analysis Applied to Regional Geochemical Data: Problems and Possibilities", Applied Geochemistry, 23(8): 2198-2213.
Theodoridis, S. ve Koutroumbas, K. (2006) "Pattern Recognition", 3rd Ed., London, Academic Press.
Vijaya, P.A., Murty, M.N. ve Subramanian, D.K. (2006) "Efficient Bottom-up Hybrid Hierarchical Clustering Techniques for Protein Sequence Classification", Pattern Recognition, 39(12): 2344- 2355.