Abstract Projective mapping (PM) or napping® has attained much attention in recent literature as a method for fast sensory profiling and measurement of consumer perception. However, little work has been done to understand the consumer’s individual differences in these experiments. In this work, segmentation criteria based on the Procrustes distance are explored. The Procrustes distance can be applied with hierarchical clustering using the Proclustrees method, which consists of doing hierarchical clustering on the pairwise Procrustes distance between consumers. An alternative strategy called sequential clusterwise rotations (SCR) is proposed. SCR extracts clusters by a sequentially partitioning obtained by combining fuzzy clustering techniques and general Procrustes analysis. The methods were tested on simulated and real data and compared with clustering based on MFA results. The simulations show that the MFA approach was outperformed by the other methods when the underlying classes were of same size and there are noise configurations present in the data. For the real data, all methods provided at last one cluster similar to the consensus but differed with respect to the number of clusters identified as well as the interpretation of the clusters. Differences between the methodologies point out the need for external cluster validation in such experiments.

中文翻译：

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投影映射中的分割

摘要 投影映射 (PM) 或小睡® 作为一种快速感官分析和消费者感知测量的方法，在最近的文献中受到了广泛关注。然而，在这些实验中，了解消费者的个体差异的工作很少。在这项工作中，探索了基于 Procrustes 距离的分割标准。Procrustes 距离可以与使用 Proclustrees 方法的层次聚类一起应用，该方法包括对消费者之间的成对 Procrustes 距离进行层次聚类。提出了一种称为连续集群旋转 (SCR) 的替代策略。SCR 通过结合模糊聚类技术和一般 Procrustes 分析获得的顺序划分来提取聚类。这些方法在模拟和真实数据上进行了测试，并与基于 MFA 结果的聚类进行了比较。模拟表明，当基础类具有相同的大小并且数据中存在噪声配置时，MFA 方法的性能优于其他方法。对于真实数据，所有方法最后提供了一个类似于共识的集群，但在识别的集群数量以及对集群的解释方面有所不同。方法之间的差异表明在此类实验中需要进行外部集群验证。所有方法最后提供了一个类似于共识的集群，但在识别的集群数量以及对集群的解释方面有所不同。方法之间的差异表明在此类实验中需要进行外部集群验证。所有方法最后提供了一个类似于共识的集群，但在识别的集群数量以及对集群的解释方面有所不同。方法之间的差异表明在此类实验中需要进行外部集群验证。