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MinPts: As a rule of thumb, a minimum minPts can be derived from the number of dimensions D in the data set, as minPts ≥ D + 1. The low value of minPts = 1 does not make sense, as then every point is a core point by definition. With minPts ≤ 2, the result will be the same as of hierarchical clustering with the single link metric, with the ...
Extracting clusters from this plot can be done manually by selecting ranges on the x-axis after visual inspection, by selecting a threshold on the y-axis (the result is then similar to a DBSCAN clustering result with the same and minPts parameters; here a value of 0.1 may yield good results), or by different algorithms that try to detect the ...
SUBCLU is an algorithm for clustering high-dimensional data by Karin Kailing, Hans-Peter Kriegel and Peer Kröger. [1] It is a subspace clustering algorithm that builds on the density-based clustering algorithm DBSCAN. SUBCLU can find clusters in axis-parallel subspaces, and uses a bottom-up, greedy strategy to remain efficient.
FOF is the special case where minPTS == 1. Although with FOF objects with less than (typically 32) points are removed from the catalogue. Based on this I propose we add a sentence somewhere in this article: a special case of DBSCAN, known as Friends-of-friends algorithm was first proposed by Davis et al 1985 and still widely used in cosmology.
The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]
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Deutsch: Illustration von Clusteranalyse mit de:DBSCAN (minPts=3). Punkte bei A sind Kernpunkte, und bilden einen Cluster. Die Punkte B und C sind keine Kernpunkte, sind aber über die Objekte bei A dichte-verbunden und daher Teil dieses Clusters.