对于水牛牛奶生产数据的K-Means和等级分类方法的比较
Lucia Trapanese1, Giovanna Bifulco1, Matteo Santinello1
1Department of Veterinary Medicine and Animal Production, University of Naples Federico II, 80137 Naples, Italy.
Animals : an open access journal from MDPI
|November 27, 2025
概括
使用测试日记录,K-means集群有效地将意大利地中海水牛分组起来,优于等级集群. 这种数据驱动的方法可以通过识别不同的动物群体来增强群体管理策略.
科学领域:
- 动物科学动物科学
- 数据科学数据科学数据科学
- 农业管理 农业管理
背景情况:
- 有效的群体管理依赖于了解动物的变异性.
- 聚类算法为数据驱动的牲畜分组提供了潜力.
研究的目的:
- 评估K-means和层次分类,以对意大利地中海水牛进行分组.
- 确定数据驱动的分组是否可以改善群体管理策略.
主要方法:
- 使用了从三个意大利地中海水牛群中例行收集的测试日记录.
- 应用K-means和层次聚类算法对组合和单个群体数据集.
- 使用轮分数,戴维斯-博尔丁指数 (DBI) 和卡林斯基-哈拉巴斯指数 (CHI) 评估集群性能.
主要成果:
- 在所有数据集中,K-means集群的表现始终优于等级集群.
- 在大多数数据集中,K-means确定了两个集群,其中一个群产生了三个集群.
- 牛奶中的几天,牛奶产量,年龄和哺乳期是区分集群的关键因素.
结论:
- K-means 聚类是基于测试日数据对水牛进行分组的优质方法.
- 已识别的动物子组可以为目标的群体管理实践提供信息.
- 这种方法支持数据驱动的决策,以改善水牛生产.
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