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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.
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.
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
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.
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 ...
Pages for logged out editors learn more. Contributions; Talk; CURE data clustering algorithm
Dr. A. Thomas McLellan, the co-founder of the Treatment Research Institute, echoed that point. “Here’s the problem,” he said. Treatment methods were determined “before anybody really understood the science of addiction. We started off with the wrong model.” For families, the result can be frustrating and an expensive failure.