Clustering datasets by means of CLUSTATIS with identification of atypical datasets. Application to sensometrics

• Published in: Food quality and preference Vol. 75; pp. 97 - 104
• Main Authors: Llobell, Fabien, Vigneau, Evelyne, Qannari, El Mostafa
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2019; Elsevier
• Subjects: Atypical subjects; Cluster analysis; data collection; Food engineering; food quality; Free sorting; Life Sciences; Mathematics; Napping; Projective mapping; Segmentation; sensometrics; simulation models; Statistics; Cluster analysis; Atypical subjects; Segmentation; Projective mapping; Napping; Free sorting
• ISSN: 0950-3293; 1873-6343
• DOI: 10.1016/j.foodqual.2019.02.017

•The focus is on the segmentation of the subjects participating to projective mapping or free sorting tasks.•A strategy of setting aside atypical subjects in the course of the segmentation is introduced.•The homogeneity of the clusters is improved by setting aside atypical subjects. A method of clustering a collection of datasets measured on the same individuals, called CLUSTATIS, was introduced and applied to the segmentation of the subjects participating in a projective mapping experiment or a free sorting task. A refinement of this method of clustering is proposed. It consists in segmenting the subjects while discarding those subjects who can be considered as atypical because they do not fit to the pattern of any cluster. This strategy of analysis requires the determination of a threshold parameter that delineates the boundary between the main clusters and the noise cluster that contains the atypical subjects. An appropriate selection of this parameter is proposed. The general strategy of analysis is illustrated on the basis of a simulation study and data from projective mapping and free sorting experiments.