Using Density and Fuzzy Clustering for Data Cleaning and Segmental Description of Livestock Data

Torgunn Aslaug Skjerve (), Gunnar Klemetsdal (), Bente Aspeholen Åby (), Jon Kristian Sommerseth (), Ulf Geir Indahl () and Hanne Fjerdingby Olsen ()
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Torgunn Aslaug Skjerve: Norwegian University of Life sciences
Gunnar Klemetsdal: Norwegian University of Life sciences
Bente Aspeholen Åby: Norwegian University of Life sciences
Jon Kristian Sommerseth: TINE SA, BTB-NMBU
Ulf Geir Indahl: Norwegian University of Life Sciences
Hanne Fjerdingby Olsen: Norwegian University of Life sciences

Journal of Agricultural, Biological and Environmental Statistics, 2025, vol. 30, issue 3, No 11, 870-885

Abstract: Abstract The cluster algorithms density-based clustering with noise and fuzzy c-means were used to edit and group a large, noisy data set from a livestock herd recording scheme consisting of slaughter records on 73,743 bulls. Density-based clustering with noise was used for data selection with $$\epsilon =0.06$$ ϵ = 0.06 and minPts $$=$$ = 8. The remaining data ( $${n} = 65{,}446$$ n = 65 , 446 ) was exposed to a fuzzy c-means analysis to partition data based on three variables: Age at slaughter, carcass weight, and average daily carcass gain. Appropriate number of clusters was chosen by the maximum value of the modified partition coefficient ( $${k} = 3$$ k = 3 clusters). Cluster validation for both hard cluster assignment and cluster membership was performed with linear models and a permutation test. The three clusters had centroid values for slaughter age and carcass weight interpreted as: Part of production systems characterized by high bull turnover (Cluster 1), production systems aiming for heavy slaughter weights (Cluster 2), and a less intensive system with higher roughage proportions (Cluster 3). The results show that the approach can be successfully combined to segment meaningful groups from large, noisy industry data, exemplified by the description of slaughter performance records. Supplementary materials accompanying this paper appear online.

Keywords: Beef production; DBSCAN; Fuzzy c-means; Norwegian red (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13253-024-00622-0

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