  The domain of cluster analysis is a meeting point for a very rich
multidisciplinary encounter, with cluster-analytic methods being studied and
developed in discrete mathematics, numerical analysis, statistics, data
analysis, data science, and computer science (including machine learning, data
mining, and knowledge discovery), to name but a few. The other side of the
coin, however, is that the domain suffers from a major accessibility problem as
well as from the fact that it is rife with division across many pretty isolated
islands. As a way out, the present paper offers a thorough and in-depth review
of the clustering domain as a whole under the form of an outline map based on
an overarching conceptual framework and a common language. With this framework
we wish to contribute to structuring the clustering domain, to characterizing
methods that have often been developed and studied in quite different contexts,
to identifying links between methods, and to introducing a frame of reference
for optimally setting up cluster analyses in data-analytic practice.
