关于在消费者感官项目中验证层次聚类的建议.
1Hungarian University of Agriculture and Life Sciences, Institute of Food Science and Technology, Department of Postharvest, Supply Chain, Commerce and Sensory Science, Villányi út, 29-43, H-1118, Budapest, Hungary.
Current research in food science
|June 2, 2023
概括
选择正确的等级聚类方法和聚类数量对于感觉数据分析至关重要. 这项研究强调,验证方法经常存在冲突,强调需要对特定数据集测试各种聚类方法.
科学领域:
- 数据科学数据科学数据科学
- 感官科学 感官科学
- 消费者研究 消费者研究
背景情况:
- 确定最佳的等级集群算法和集群计数是消费者感官项目中持续存在的挑战.
- 研究人员经常忽略了他们选择的距离指标,链接规则和集群号的理由.
- 集群验证技术的不一致结果使选择过程复杂化,使严格的评估耗时.
研究的目的:
- 调查不同距离指标,链接规则和集群号对感觉数据集等级集群结果的影响.
- 评估各种集群验证方法在应用于传感数据时的一致性和可靠性.
- 为选择适当的集群参数提供指导,用于消费者感官分析.
主要方法:
- 使用各种距离指标 (例如,欧几里德) 和链接规则 (例如,沃德的方法) 进行了三种不同的感官数据集的聚类.
- 对于每个数据集和集群组合,系统测试了不同数量的集群.
- 使用标准集群验证技术来评估产生的集群的质量和稳定性.
主要成果:
- 集群验证方法在测试的感官数据集中产生了矛盾的结果.
- 发现最佳的集群配置高度依赖于每个数据集的特定特征.
- 虽然欧几里德距离与沃德的方法是常见的选择,但它不是普遍最佳的.
结论:
- 层次聚类参数的选择显著影响传感数据分析的结果.
- 没有单一的最佳集群方法;验证是特定于数据集的.
- 强烈建议对不同的集群组合进行彻底的测试和验证,以获得强大的消费者感官项目结果.
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