  A novel method, termed Reduced Dimensionality Cluster Identification, RDCI,
is presented, for the identification and quantitative description of clusters
formed by N objects in three dimensional space. The method consists of finding
a path, as short as possible, connecting the objects, and then tracking down
the size s, of a subgroup i-n, i-n+1, ..., i+n, of 2n+1 < N particles for i
varying from n+1 to N-n. Clusters are located where local minima of s(i) occur
whereas local maxima serve as delimiters partitioning the path in subsets
containing the clusters. Minimal post-processing allows for the removal of
outliers on the basis of user-defined criteria and the identification of
clearly defined clusters. The advantage of the method is that it requires no
predetermined input or criteria of "clusterness" such as number of objects or
size of aggregates. Among the numerous possible applications of the method,
results are herein reported, from Molecular Dynamics simulations of a binary
mixture of Lennard-Jones fluids and of a model polymeric system with a gas-like
substance dispersed in it. The method is shown to allow the extraction of
meaningful quantitative information regarding the tendency of molecules to
cluster or de-cluster and the dynamics of the clustering processes.
