In some applications and in order to better address real-world situations, data can be known through pairwise measures of resemblance or difference between the objects of interest (similarities, dissimilarities, kernels, networks...). This talk will describe a general framework to deal with such data, especially focusing on the unsupervised setting and exploratory analyses. Also, solutions for combining multiple relational data - each providing a different view on a specific aspect of the data - will be described. The talk will provide an overview of applications of this framework to self-organizing maps (R package SOMbrero), constrained hierarchical clustering (R package adjclust) and PCA (R package mixKernel), with illustrations on case studies in the fields of biology and social sciences.