Similarity and Dissimilarity

Several classical or symbolic data analysis techniques start from the assumption that there are some means for assessing and quantifying the similarities (or dissimilarities) which may exist between the underlying objects (individuals, classes, symbolic objects, etc.), by a recourse to the observed data matrix. They use these similarities as their data input. For example, in cluster analysis where we look for ‘homogeneous’ classes C₁, C₂,… of objects, it is typically required that pairs of objects from the saine class have a large similarity (i.e., a small dissimilarity) and, conversely, that the similarity is small for pairs of objects fromdifferent classes (see Section 11.1).