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  2. CURE clustering algorithm - Wikipedia

    en.wikipedia.org/wiki/CURE_algorithm

    To avoid the problems with non-uniform sized or shaped clusters, CURE employs a hierarchical clustering algorithm that adopts a middle ground between the centroid based and all point extremes. In CURE, a constant number c of well scattered points of a cluster are chosen and they are shrunk towards the centroid of the cluster by a fraction α.

  3. Category:Cluster analysis algorithms - Wikipedia

    en.wikipedia.org/wiki/Category:Cluster_analysis...

    This category contains algorithms used for cluster analysis. Pages in category "Cluster analysis algorithms" The following 42 pages are in this category, out of 42 total.

  4. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters).

  5. CURE data clustering algorithm - Wikipedia

    en.wikipedia.org/?title=CURE_data_clustering...

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  6. Category:Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Category:Cluster_analysis

    WACA clustering algorithm This page was last edited on 14 May 2021, at 07:19 (UTC). Text is available under the Creative Commons Attribution-ShareAlike ...

  7. Data stream clustering - Wikipedia

    en.wikipedia.org/wiki/Data_stream_clustering

    In computer science, data stream clustering is defined as the clustering of data that arrive continuously such as telephone records, multimedia data, financial transactions etc. Data stream clustering is usually studied as a streaming algorithm and the objective is, given a sequence of points, to construct a good clustering of the stream, using a small amount of memory and time.

  8. Determining the number of clusters in a data set - Wikipedia

    en.wikipedia.org/wiki/Determining_the_number_of...

    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]

  9. 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.