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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 Spherical k-means clustering algorithm is suitable for textual data. [37] Hierarchical variants such as Bisecting k-means, [38] X-means clustering [39] and G-means clustering [40] repeatedly split clusters to build a hierarchy, and can also try to automatically determine the optimal number of clusters in a dataset.
In statistics and data mining, X-means clustering is a variation of k-means clustering that refines cluster assignments by repeatedly attempting subdivision, and keeping the best resulting splits, until a criterion such as the Akaike information criterion (AIC) or Bayesian information criterion (BIC) is reached. [5]
As listed above, clustering algorithms can be categorized based on their cluster model. The following overview will only list the most prominent examples of clustering algorithms, as there are possibly over 100 published clustering algorithms. Not all provide models for their clusters and can thus not easily be categorized.
The final step for the BoW model is to convert vector-represented patches to "codewords" (analogous to words in text documents), which also produces a "codebook" (analogy to a word dictionary). A codeword can be considered as a representative of several similar patches. One simple method is performing k-means clustering over all the vectors. [7]
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Clustering Density-Based Clustering; Fuzzy C-Means Clustering; Hierarchical Clustering; Model-based clustering; Neighborhood-based Clustering (i.e., K-Means Clustering, K-Medians clustering, K-Medoids clustering) Random Forest Clustering; Meta Analysis: Synthesise evidence across multiple studies. Includes techniques for fixed and random ...
In applied mathematics, k-SVD is a dictionary learning algorithm for creating a dictionary for sparse representations, via a singular value decomposition approach. k-SVD is a generalization of the k-means clustering method, and it works by iteratively alternating between sparse coding the input data based on the current dictionary, and updating the atoms in the dictionary to better fit the data.