TY  - JOUR
T1  - K-Mean Clustering of Holstein Friesian Dairy Cattle using Genomic Breeding Values
TT  - K-Mean Clustering of Holstein Friesian Dairy Cattle using Genomic Breeding Values
AU  - Önder, Hasan
AU  - Hoşgönül, Buğra
PY  - 2025
DA  - January
Y2  - 2025
DO  - 10.34248/bsengineering.1601851
JF  - Black Sea Journal of Engineering and Science
JO  - BSJ Eng. Sci.
PB  - Karyay Karadeniz Yayımcılık Ve Organizasyon Ticaret Limited Şirketi
WT  - DergiPark
SN  - 2619-8991
SP  - 263
EP  - 267
VL  - 8
IS  - 1
LA  - en
AB  - Clustering refers to algorithms to uncover such clusters in unlabeled data. Data points belonging to the same cluster exhibit similar features, whereas data points from different clusters are dissimilar to each other. The identification of such clusters leads to segmentation of data points into a number of distinct groups. In this study it was aimed to classify the 492 Holstein Friesian dairy cattle with determining the optimum number of clusters using the genomic breeding values (GBVs) calculated with 13250 SNPs using GBLUP for milk yield (kg), milk fat (%), milk protein (%), milk lactose (%), and milk dry matter (%). Results showed that the optimum number cluster was determined as two for the genomic breeding values. Determining the most appropriate number of clusters, it provides great convenience in the selection of breeding animals after determining the animals that can provide optimum efficiency in the herd or the animals that need to be eliminated from the existing herd. As a result, it can be said that the k-means method can be used successfully in clustering animals for genomic breeding values, but for this, at first, the optimum number of clusters must be determined.
KW  - K-mean clustering
KW  - Breeding value
KW  - Genomic selection
KW  - Dairy cattle
N2  - Clustering refers to algorithms to uncover such clusters in unlabeled data. Data points belonging to the same cluster exhibit similar features, whereas data points from different clusters are dissimilar to each other. The identification of such clusters leads to segmentation of data points into a number of distinct groups. In this study it was aimed to classify the 492 Holstein Friesian dairy cattle with determining the optimum number of clusters using the genomic breeding values (GBVs) calculated with 13250 SNPs using GBLUP for milk yield (kg), milk fat (%), milk protein (%), milk lactose (%), and milk dry matter (%). Results showed that the optimum number cluster was determined as two for the genomic breeding values. Determining the most appropriate number of clusters, it provides great convenience in the selection of breeding animals after determining the animals that can provide optimum efficiency in the herd or the animals that need to be eliminated from the existing herd. As a result, it can be said that the k-means method can be used successfully in clustering animals for genomic breeding values, but for this, at first, the optimum number of clusters must be determined.
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CR  - Önder H, Sitskowska B, Kurnaz B, Piwczynski D, Kolenda M, Sen U, Tırınk C, Çanga Boğa D. 2023. Multi-trait single-step genomic prediction for milk yield and milk components for Polish Holstein population. Animals, 13: 3070. https://doi.org/10.3390/ani13193070
UR  - https://doi.org/10.34248/bsengineering.1601851
L1  - https://dergipark.org.tr/en/download/article-file/4442077
ER  -