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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.
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 ...
In computer programming, primary clustering is a phenomenon that causes performance degradation in linear-probing hash tables.The phenomenon states that, as elements are added to a linear probing hash table, they have a tendency to cluster together into long runs (i.e., long contiguous regions of the hash table that contain no free slots).
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. Centroid model s: for example, the k-means algorithm represents each cluster by a single mean vector.
Key or hash function should avoid clustering, the mapping of two or more keys to consecutive slots. Such clustering may cause the lookup cost to skyrocket, even if the load factor is low and collisions are infrequent. The popular multiplicative hash [1] is claimed to have particularly poor clustering behaviour. [2]
In data mining, k-means++ [1] [2] is an algorithm for choosing the initial values (or "seeds") for the k-means clustering algorithm. It was proposed in 2007 by David Arthur and Sergei Vassilvitskii, as an approximation algorithm for the NP-hard k-means problem—a way of avoiding the sometimes poor clusterings found by the standard k-means algorithm.
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]
The minimum disagreement correlation clustering problem is the following optimization problem: + + (). Here, the set + contains the attractive edges whose endpoints are in different components with respect to the clustering and the set () contains the repulsive edges whose endpoints are in the same component with respect to the clustering .