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Every data mining task has the problem of parameters. Every parameter influences the algorithm in specific ways. For DBSCAN, the parameters ε and minPts are needed. The parameters must be specified by the user. Ideally, the value of ε is given by the problem to solve (e.g. a physical distance), and minPts is then the desired minimum cluster ...
Like DBSCAN, OPTICS requires two parameters: ε, which describes the maximum distance (radius) to consider, and MinPts, describing the number of points required to form a cluster. A point p is a core point if at least MinPts points are found within its ε-neighborhood () (including point p itself).
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.
GPL-V3 Java implementations of more than 80 data mining algorithms. It offers an efficient Java implementation of the DBScan algorithm using a KD-Tree that is easy to integrate in other Java software. A GUI and command line interface is provided. This is advertisement text.
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Deutsch: Illustration von Clusteranalyse mit de:DBSCAN (minPts=3). Punkte bei A sind Kernpunkte, und bilden einen Cluster. 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.