Abstract The Clustering around Latent Variables (CLV) method may be used to identify segments of consumers in sensory liking studies. To date, this method has been unable to deal with missing data. Thus, consumers with missing data had to be discarded or a data imputation technique had to be applied beforehand. The CLV algorithms have recently been updated in order to perform clustering and local imputations simultaneously. Firstly, a simulation study where missing data were generated randomly or according to a balanced incomplete block design was set up. It makes it possible to assess the ability of the CLV procedure to identify segments of consumers in a hedonic dataset with missing data, in comparison with those segments obtained on the basis of a complete data set. The method was also applied to the 13th Sensometrics Conference workshop data, associated with a preference experiment for which 62.5% of the liking scores was missing, according to a sensory informed incomplete block design. The results of the CLV procedure are discussed and compared to those of the Gaussian mixture model based approach previously proposed to solve this issue of simultaneous imputation and clustering. It turns out that both strategies led to two segments of consumers, somewhat different in terms of individual cluster membership but with fairly convergent averaged liking profiles regarding the most discriminant products.

中文翻译：

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对缺失数据的一组消费者进行细分

摘要 潜变量聚类 (CLV) 方法可用于在感官喜好研究中识别消费者细分。迄今为止，这种方法一直无法处理缺失的数据。因此，必须丢弃丢失数据的消费者，或者必须事先应用数据插补技术。CLV 算法最近已更新，以便同时执行聚类和本地插补。首先，建立了随机生成缺失数据或根据平衡不完整块设计生成的模拟研究。与基于完整数据集获得的那些细分相比，它可以评估 CLV 程序在具有缺失数据的享乐数据集中识别消费者细分的能力。该方法还应用于第 13 届 Sensometrics Conference 研讨会数据，根据感官告知的不完全块设计，与偏好实验相关联，其中 62.5% 的喜好分数缺失。讨论了 CLV 过程的结果，并将其与先前提出的基于高斯混合模型的方法的结果进行比较，以解决同时插补和聚类的问题。事实证明，这两种策略都导致了两个消费者群体，在个体集群成员方面有些不同，但对于最具辨别力的产品具有相当趋同的平均喜好特征。讨论了 CLV 过程的结果，并将其与先前提出的基于高斯混合模型的方法的结果进行比较，以解决同时插补和聚类的问题。事实证明，这两种策略都导致了两个消费者群体，在个体集群成员方面有些不同，但对于最具辨别力的产品具有相当趋同的平均喜好特征。讨论了 CLV 过程的结果，并将其与先前提出的基于高斯混合模型的方法的结果进行比较，以解决同时插补和聚类的问题。事实证明，这两种策略都导致了两个消费者群体，在个体集群成员方面有些不同，但对于最具辨别力的产品具有相当趋同的平均喜好特征。