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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.
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.
Data stream clustering has recently attracted attention for emerging applications that involve large amounts of streaming data. For clustering, k-means is a widely used heuristic but alternate algorithms have also been developed such as k-medoids, CURE and the popular [citation needed] BIRCH.
The most appropriate clustering algorithm for a particular problem often needs to be chosen experimentally, unless there is a mathematical reason to prefer one cluster model over another. An algorithm that is designed for one kind of model will generally fail on a data set that contains a radically different kind of model. [5]
By Leah Douglas and Julie Steenhuysen (Reuters) -California's public health department reported a possible case of bird flu in a child with mild respiratory symptoms on Tuesday, but said there was ...
Marburg is a rare but “severe hemorrhagic fever that can cause serious illness and death,” the U.S. Centers for Disease Control says, adding that there is no treatment or vaccine for it.
Automatic clustering algorithms are algorithms that can perform clustering without prior knowledge of data sets. In contrast with other cluster analysis techniques, automatic clustering algorithms can determine the optimal number of clusters even in the presence of noise and outlier points. [1] [needs context]
Collage of political strategist, Alex Bruesewitz, and other men in suits including Donald Trump, at the moment Bruesewitz collapsed during a speech.Alex Bruesewitz, a top adviser for President ...