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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).
Different Gaussian model-based clustering methods have been developed with an eye to handling high-dimensional data. These include the pgmm method, [11] which is based on the mixture of factor analyzers model, and the HDclassif method, based on the idea of subspace clustering. [12]
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
This type of algorithm provides different methods to find clusters in the data. The fastest method is DBSCAN, which uses a defined distance to differentiate between dense groups of information and sparser noise. Moreover, HDBSCAN can self-adjust by using a range of distances instead of a specified one.
In the theory of cluster analysis, the nearest-neighbor chain algorithm is an algorithm that can speed up several methods for agglomerative hierarchical clustering.These are methods that take a collection of points as input, and create a hierarchy of clusters of points by repeatedly merging pairs of smaller clusters to form larger clusters.
Different text mining methods are used based on their suitability for a data set. Text mining is the process of extracting data from unstructured text and finding patterns or relations. Below is a list of text mining methodologies. Centroid-based Clustering: Unsupervised learning method. Clusters are determined based on data points. [1]
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.
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.