Clustering consumer data reveals important information to help refine products for specific market segments. There are compelling reasons to use incomplete block designs to collect consumer data; however, this presents the challenge of dealing with missing data. The purpose of this workshop is to investigate the effect of different imputation techniques on the results of cluster analysis of balanced-incomplete-block (BIB)
Many sensory experiences have a temporal dimension, and several approaches have been proposed to capture changes in sensations with time. The workshop will review a few of those methods, including Time intensity, Temporal Attribute Discrimination, Progressive Profiling, Sequential Profiling, Temporal Dominance of Sensations, and Temporal Order of Sensations.