A Rodriguez, A Laio,
Clustering by fast search and find of density peaks
SCIENCE, 322, 1492 (2014)
Cluster analysis is aimed at classifying elements into categories on the basis of their
similarity. Its applications range from astronomy to bioinformatics, bibliometrics, and pattern
recognition. We propose an approach based on the idea that cluster centers are characterized
by a higher density than their neighbors and by a relatively large distance from points with
higher densities. This idea forms the basis of a clustering procedure in which the number of
clusters arises intuitively, outliers are automatically spotted and excluded from the analysis, and
clusters are recognized regardless of their shape and of the dimensionality of the space in which
they are embedded. We demonstrate the power of the algorithm on several test cases.