  OPTICS is a density-based clustering algorithm that performs well in a wide
variety of applications. For a set of input objects, the algorithm creates a
so-called reachability plot that can be either used to produce cluster
membership assignments, or interpreted itself as an expressive two-dimensional
representation of the density-based clustering structure of the input set, even
if the input set is embedded in higher dimensions. The main focus of this work
is a visualization method that can be used to assign colours to all entries of
the input database, based on hierarchically represented a-priori knowledge
available for each of these objects. Based on two different,
bioinformatics-related applications we illustrate how the proposed method can
be efficiently used to identify clusters with proven real-life relevance.
