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A novel method, termed Reduced Dimensionality Cluster Identification, RDCI,is presented, for the identification and quantitative description of clustersformed by N objects in three dimensional space. The method consists of findinga path, as short as possible, connecting the objects, and then tracking downthe size s, of a subgroup i-n, i-n+1, ..., i+n, of 2n+1 < N particles for ivarying from n+1 to N-n. Clusters are located where local minima of s(i) occurwhereas local maxima serve as delimiters partitioning the path in subsetscontaining the clusters. Minimal post-processing allows for the removal ofoutliers on the basis of user-defined criteria and the identification ofclearly defined clusters. The advantage of the method is that it requires nopredetermined input or criteria of "clusterness" such as number of objects orsize of aggregates. Among the numerous possible applications of the method,results are herein reported, from Molecular Dynamics simulations of a binarymixture of Lennard-Jones fluids and of a model polymeric system with a gas-likesubstance dispersed in it. The method is shown to allow the extraction ofmeaningful quantitative information regarding the tendency of molecules tocluster or de-cluster and the dynamics of the clustering processes.

RDCI: A novel method of cluster analysis and applications thereof in sample molecular simulations