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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. SUBCLU - Wikipedia

    en.wikipedia.org/wiki/SUBCLU

    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.

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  8. Talk:DBSCAN - Wikipedia

    en.wikipedia.org/wiki/Talk:DBSCAN

    If I'm not mistaken, then given the definition or core points, either the label is wrong (minPts = 4 should be minPts = 3) OR the figure is wrong and only two points should be considered as core points (I guess correcting the caption is easier than correcting the figure).

  9. List of text mining methods - Wikipedia

    en.wikipedia.org/wiki/List_of_text_mining_methods

    DBSCAN; Distribution-based Clustering: Clusters are formed based on mathematical methods from data. [1] Expectation-maximization algorithm; Collocation; Stemming Algorithm. Truncating Methods: Removing the suffix or prefix of a word. Lovins Stemmer: Removes longest suffix. Porters Stemmer: Allows programmers to stem words based on their own ...