Abstract The STATIS method has been successfully applied to the analysis of sensory profiling data and other kinds data in sensometrics. We discuss its use and benefits and compare its outcomes to alternative methods for the analysis of multiblock data arising in situations such as projective mapping and free sorting experiments. More importantly, a method of clustering a collection of datasets measured on the same individuals, called CLUSTATIS, is introduced. It is based on the optimization of a criterion and consists in a hierarchical cluster analysis and a partitioning algorithm akin to the K-means algorithm. The procedure of analysis can be seen as an extension of the cluster analysis of variables around latent components (CLV, Vigneau & Qannari, 2003) to the case of blocks of variables. Alongside the determination of the clusters, a latent configuration is determined by the STATIS method. The interest of CLUSTATIS in sensometrics is discussed and illustrated on the basis of two case studies pertaining to the projective mapping also called Napping and the free sorting tasks, respectively.

中文翻译：

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通过 statis 和 clustatis 方法对多块数据集进行分析和聚类。感测学的应用

摘要 STATIS 方法已成功应用于感官分析数据和传感测量学中的其他类型数据的分析。我们讨论了它的用途和好处，并将其结果与在投影映射和自由排序实验等情况下分析多块数据的替代方法进行了比较。更重要的是，引入了一种对同一个人测量的数据集进行聚类的方法，称为 CLUSTATIS。它基于标准的优化，包括层次聚类分析和类似于 K-means 算法的划分算法。分析过程可以看作是围绕潜在成分的变量聚类分析（CLV，Vigneau & Qannari，2003）对变量块情况的扩展。除了集群的确定，潜在配置由 STATIS 方法确定。CLUSTATIS 在传感测量学中的兴趣在两个案例研究的基础上进行了讨论和说明，这些案例研究分别与投影映射有关，也称为小睡和自由分类任务。