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Siyah Alacalar’ın K-Ortalamalı Kümeleme Yöntemi İle Sınıflandırılması

Year 2016, , 147 - 151, 22.06.2016
https://doi.org/10.20289/zfdergi.388904

Abstract










 






Kümeleme  analizi ile birim veya değişkenler sahip
oldukları özellikler bakımından benzerlik veya farklılıklarına göre
gruplandırılabilmektedir. Bu araştırmada 4496 adet Siyah Alaca inek, sürü,
ilkine buzağılama yaşı, laktasyon süresi ve 305 günlük süt verimi bakımından
aşamalı (hiyerarşik) olmayan k-ortalamalı (k-means) kümeleme yöntemi ile iki, üç ve dört kümeye gruplandırılmıştır.
Siyah Alacalar’ın kümelere ayrılmasında inceleme konusu özelliklerin
istatistiksel olarak etkili oldukları saptanmıştır (P<0.01). Yine bu
özelliklere göre Siyah Alacalar’ın üç farklı kümeye ayrıldığı ve bu üç kümenin
istatistiksel olarak farklı olduğu belirlenmiştir (P<0.01). Siyah
Alacalar’ın doğru sınıflandırma oranı ise %98 olarak bulunmuştur. Süt veriminin
en yüksek, ilkine buzağılama yaşının en düşük ve ideal olan 305 günlük
laktasyon süresine sahip üçüncü kümedeki Siyah Alacalar’ın ıslah çalışmalarında
kullanılması önerilmiştir.






References

  • Akıllı A and H. Atıl. 2013. Classification of milk yield characteristics with cluster analysis. 7th International Balkan Animal Conference, BALNIMALCON. Namık Kemal University, Faculty of Agriculture, Department of Animal Science, 3-5 October, Tekirdağ, Turkey.
  • Chernoff H. 1972. Metric considerations in cluster analysis. Proceedings of the 7th Berkeley. Symposium on Mathematical Statistics and Probability. 1:621-629.
  • Çokluk Ö., G. Şekercioğlu and S. Büyüköztürk. 2010. Multivariable Statistics for Social Sciences (In Turkish). Pegem Akademi Yayıncılık, Ankara.
  • Görgülü Ö. 2010. Classification of dairy cattle in terms of some milk yield characteristics using by fuzzy clustering. Journal of Animal and Veterinary Advances 9:1947-1951 DOI 10.3923/javaa.2010. 1947.1951.
  • Gürcan S. and H. Akçapınar. 2002. Alman et ve Karacabey Merinosu koyunlarının canlı ağırlık, vücut ölçüleri ve yapağı inceliği yönünden kümeleme analizi ile incelenmesi. Turkish Journal of Veterinary and Animal Sciences 26:1255-1261.
  • Hair J, B. Black, B. Babin, R. Anderson and R. Tatham. 2006. Multivariate Data Analysis. 6th ed. Prentice Hall, Upper Saddle River, New Jersey.
  • Johnson R.A. and D.W. Wichern. 2005. Applied Multivariate Statistical Analysis. 5th ed. Prentice Hall, Upper Saddle River, New Jersey.
  • Kılıç İ. ve C. Özbeyaz. 2010. Bulanık kümeleme analizinin koyun yetiştiriciliğinde kullanımı ve bir uygulama. Kocatepe Veterinary Journal 3: 31-37.
  • Küçükönder H, E. Efe, E. Akyol, M. Şahin ve F. Üçkardeş. 2004. Çok değişkenli istatistiksel analizlerin hayvancılıkta kullanımı. 4th National Animal Science Congress, 1-3 September, Isparta, Turkey.
  • Küçükönder H, T. Ayaşan and H. Hızlı. 2015. Classification of Holstein dairy cattles in terms of parameters some milk component belongs by using the fuzzy cluster analysis. Kafkas Universitesi Veteriner Fakültesi Dergisi, 23: 601-606 DOI 10.9775/kvfd.2015.12987.
  • McMahon R.G.P. 2001. Deriving an empirical development taxonomy for manufacturing smes using data from Australia’s business longitudinal survey. Small Business Economics 17:197–212.
  • Ruttner F, L. Tassencour and J. Louveaux. 1978. Biometrical statistical analysis of the geographic variability of Apis Mellifera L. Apidologie 9:363-381.
  • Singh N. and D. Singh. 2012. Performance evaluation of k-means and heirarichal clustering in terms of accuracy and running time. International Journal of Computer Science and Information Technologies 3:4119-4121.
  • Tabachnick B.G. and L.S. Fidell. 2007. Using Multivariate Statistics. 5th ed. Pearson.
  • Vogt W. and D. Nagel. 1992. Cluster analysis in diagnosis. Clinical Chemistry 38:182–198.
  • Yakubu A. and S.B. Ugbo. 2011. An assessment of biodiversity in morphological traits of Muscovy ducks in Nigeria using discriminant analysis. International Conference on Biology, Environment and Chemistry, Singapore, 1:389-391.
  • Yim O. and K.T. Ramdeen. 2015. Hierarchical cluster analysis: comparison of three linkage measures and application to psychological data. The Quantitative Methods for Psychology 1:8-21.

Clustering of Holstein Friesians Using K-Means Method

Year 2016, , 147 - 151, 22.06.2016
https://doi.org/10.20289/zfdergi.388904

Abstract

By  cluster analysis units or variables can be grouped according to
similarities or differences in terms of their properties. In this study total
of 4496 Holstein Friesian cows were grouped two, three and four clusters
according to their herd, age at first calving, lactation length and 305 day
milk yields. Nonhierarchical k-means clustering technique is used for this
purpose. Related traits were found statically significant for clustering of
Holsteins (P<0.01). Holsteins were divided into three clusters and these
clusters were found statistically different
(P<0.01). The correct classification
percentage of cows was 98%. In the third cluster Holsteins which have the highest
milk yield, the lowest age of first calving and 305-day lactation period were
proposed for breeding programs.

References

  • Akıllı A and H. Atıl. 2013. Classification of milk yield characteristics with cluster analysis. 7th International Balkan Animal Conference, BALNIMALCON. Namık Kemal University, Faculty of Agriculture, Department of Animal Science, 3-5 October, Tekirdağ, Turkey.
  • Chernoff H. 1972. Metric considerations in cluster analysis. Proceedings of the 7th Berkeley. Symposium on Mathematical Statistics and Probability. 1:621-629.
  • Çokluk Ö., G. Şekercioğlu and S. Büyüköztürk. 2010. Multivariable Statistics for Social Sciences (In Turkish). Pegem Akademi Yayıncılık, Ankara.
  • Görgülü Ö. 2010. Classification of dairy cattle in terms of some milk yield characteristics using by fuzzy clustering. Journal of Animal and Veterinary Advances 9:1947-1951 DOI 10.3923/javaa.2010. 1947.1951.
  • Gürcan S. and H. Akçapınar. 2002. Alman et ve Karacabey Merinosu koyunlarının canlı ağırlık, vücut ölçüleri ve yapağı inceliği yönünden kümeleme analizi ile incelenmesi. Turkish Journal of Veterinary and Animal Sciences 26:1255-1261.
  • Hair J, B. Black, B. Babin, R. Anderson and R. Tatham. 2006. Multivariate Data Analysis. 6th ed. Prentice Hall, Upper Saddle River, New Jersey.
  • Johnson R.A. and D.W. Wichern. 2005. Applied Multivariate Statistical Analysis. 5th ed. Prentice Hall, Upper Saddle River, New Jersey.
  • Kılıç İ. ve C. Özbeyaz. 2010. Bulanık kümeleme analizinin koyun yetiştiriciliğinde kullanımı ve bir uygulama. Kocatepe Veterinary Journal 3: 31-37.
  • Küçükönder H, E. Efe, E. Akyol, M. Şahin ve F. Üçkardeş. 2004. Çok değişkenli istatistiksel analizlerin hayvancılıkta kullanımı. 4th National Animal Science Congress, 1-3 September, Isparta, Turkey.
  • Küçükönder H, T. Ayaşan and H. Hızlı. 2015. Classification of Holstein dairy cattles in terms of parameters some milk component belongs by using the fuzzy cluster analysis. Kafkas Universitesi Veteriner Fakültesi Dergisi, 23: 601-606 DOI 10.9775/kvfd.2015.12987.
  • McMahon R.G.P. 2001. Deriving an empirical development taxonomy for manufacturing smes using data from Australia’s business longitudinal survey. Small Business Economics 17:197–212.
  • Ruttner F, L. Tassencour and J. Louveaux. 1978. Biometrical statistical analysis of the geographic variability of Apis Mellifera L. Apidologie 9:363-381.
  • Singh N. and D. Singh. 2012. Performance evaluation of k-means and heirarichal clustering in terms of accuracy and running time. International Journal of Computer Science and Information Technologies 3:4119-4121.
  • Tabachnick B.G. and L.S. Fidell. 2007. Using Multivariate Statistics. 5th ed. Pearson.
  • Vogt W. and D. Nagel. 1992. Cluster analysis in diagnosis. Clinical Chemistry 38:182–198.
  • Yakubu A. and S.B. Ugbo. 2011. An assessment of biodiversity in morphological traits of Muscovy ducks in Nigeria using discriminant analysis. International Conference on Biology, Environment and Chemistry, Singapore, 1:389-391.
  • Yim O. and K.T. Ramdeen. 2015. Hierarchical cluster analysis: comparison of three linkage measures and application to psychological data. The Quantitative Methods for Psychology 1:8-21.
There are 17 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Çiğdem Takma This is me

Öznur İşçi Güneri This is me

Yavuz Akbaş This is me

Publication Date June 22, 2016
Submission Date November 6, 2015
Acceptance Date February 29, 2016
Published in Issue Year 2016

Cite

APA Takma, Ç., İşçi Güneri, Ö., & Akbaş, Y. (2016). Clustering of Holstein Friesians Using K-Means Method. Journal of Agriculture Faculty of Ege University, 53(2), 147-151. https://doi.org/10.20289/zfdergi.388904

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