Density Based Spatial Clustering of Applications of Noise Uses a density-based notion of clusters to discover clusters of arbitrary shapes, in spatial databases Key idea: for each object of a cluster, the neighborhood of a given radius contains at least a minimum number of data-objects. (i.e. the density of each cluster must exceed a threshold value) Choosing the distance function is the critical parameter. An object that appears to be part of Noise at present, might, at a later stage, be included into one of the clusters.

dbscan.h
distance.h
test_dbscan.cpp
db_scan.cpp
clusters.h
clusters.cpp
adiacency_matrix.h

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