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  2. DBSCAN - Wikipedia

    en.wikipedia.org/wiki/DBSCAN

    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 dendrogram cut at height ε. Therefore, minPts must be chosen at least 3. However, larger values are usually better for data sets ...

  3. OPTICS algorithm - Wikipedia

    en.wikipedia.org/wiki/OPTICS_algorithm

    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 N ε ( p ) {\displaystyle N_{\varepsilon }(p)} (including point p itself).

  4. Talk:DBSCAN - Wikipedia

    en.wikipedia.org/wiki/Talk:DBSCAN

    DBSCAN(D, eps, MinPts) C = 0 for each unvisited point P in dataset D mark P as visited N = getNeighbors (P, eps) if sizeof(N) < MinPts mark P as NOISE else C = next ...

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  7. File:DBSCAN-Illustration.svg - Wikipedia

    en.wikipedia.org/wiki/File:DBSCAN-Illustration.svg

    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.

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  9. File:DBSCAN-Gaussian-data.svg - Wikipedia

    en.wikipedia.org/wiki/File:DBSCAN-Gaussian-data.svg

    English: Cluster analysis with DBSCAN on a gaussian-distributed-based data set. Even with carefully chosen parameters minPts and ε {\displaystyle \varepsilon } , DBSCAN is unable to capture all clusters correctly at the same time, since the difference in density is too high and the separation too low.