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  2. DBSCAN - Wikipedia

    en.wikipedia.org/wiki/DBSCAN

    DBSCAN is also used as part of subspace clustering algorithms like PreDeCon and SUBCLU. HDBSCAN* [ 6 ] [ 7 ] is a hierarchical version of DBSCAN which is also faster than OPTICS, from which a flat partition consisting of the most prominent clusters can be extracted from the hierarchy.

  3. Automatic clustering algorithms - Wikipedia

    en.wikipedia.org/.../Automatic_Clustering_Algorithms

    BIRCH (balanced iterative reducing and clustering using hierarchies) is an algorithm used to perform connectivity-based clustering for large data-sets. [7] It is regarded as one of the fastest clustering algorithms, but it is limited because it requires the number of clusters as an input.

  4. scikit-learn - Wikipedia

    en.wikipedia.org/wiki/Scikit-learn

    scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...

  5. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    The notion of a cluster, as found by different algorithms, varies significantly in its properties. Understanding these "cluster models" is key to understanding the differences between the various algorithms. Typical cluster models include: Connectivity model s: for example, hierarchical clustering builds models based on distance connectivity.

  6. CURE algorithm - Wikipedia

    en.wikipedia.org/wiki/CURE_algorithm

    CURE (Clustering Using REpresentatives) is an efficient data clustering algorithm for large databases [citation needed]. Compared with K-means clustering it is more robust to outliers and able to identify clusters having non-spherical shapes and size variances.

  7. Complete-linkage clustering - Wikipedia

    en.wikipedia.org/wiki/Complete-linkage_clustering

    Complete-linkage clustering is one of several methods of agglomerative hierarchical clustering. At the beginning of the process, each element is in a cluster of its own. The clusters are then sequentially combined into larger clusters until all elements end up being in the same cluster. The method is also known as farthest neighbour clustering.

  8. Correlation clustering - Wikipedia

    en.wikipedia.org/wiki/Correlation_clustering

    The authors show that the above algorithm is a 3-approximation algorithm for correlation clustering. The best polynomial-time approximation algorithm known at the moment for this problem achieves a ~2.06 approximation by rounding a linear program, as shown by Chawla , Makarychev, Schramm, and Yaroslavtsev .

  9. Hierarchical clustering - Wikipedia

    en.wikipedia.org/wiki/Hierarchical_clustering

    The standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of () and requires () memory, which makes it too slow for even medium data sets. . However, for some special cases, optimal efficient agglomerative methods (of complexity ()) are known: SLINK [2] for single-linkage and CLINK [3] for complete-linkage clusteri