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k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which...

process of actually solving the clustering problem. For a certain class of clustering algorithms (in particular k-means, k-medoids and expectation–maximization...

In data mining, k-means++ is an algorithm for choosing the initial values (or "seeds") for the k-means clustering algorithm. It was proposed in 2007 by...

clustering (also referred to as soft clustering or soft k-means) is a form of clustering in which each data point can belong to more than one cluster...

greedy manner. The results of hierarchical clustering are usually presented in a dendrogram. Hierarchical clustering has the distinct advantage that any valid...

k-medians clustering is a cluster analysis algorithm. It is a variation of k-means clustering where instead of calculating the mean for each cluster to determine...

have a low or negative value, then the clustering configuration may have too many or too few clusters. A clustering with an average silhouette width of over...

statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter...

The general approach to spectral clustering is to use a standard clustering method (there are many such methods, k-means is discussed below) on relevant...

Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions. Such high-dimensional...

worth the additional cost. In clustering, this means one should choose a number of clusters so that adding another cluster doesn't give much better modeling...

Hierarchical Clustering Around Medoids (HACAM), which uses medoids in hierarchical clustering From the definition above, it is clear that the medoid of a set X {\displaystyle...

(Clustering Using REpresentatives) is an efficient data clustering algorithm for large databases[citation needed]. Compared with K-means clustering it...

Unix. X with diacritics: Ẍ ẍ Ẋ ẋ X̂ x̂ ᶍ IPA-specific symbols related to X: χ Teuthonista phonetic transcription-specific symbols related to X: U+AB56...

of the clustering in the network, whereas the local gives an indication of the extent of "clustering" of a single node. The local clustering coefficient...

iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large...

evaluation metric, where the assessment of the clustering quality is based solely on the dataset and the clustering results, and not on external, ground-truth...

Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg...

In computer science, data stream clustering is defined as the clustering of data that arrive continuously such as telephone records, multimedia data,...

basis for clustering, and ways to choose the number of clusters, to choose the best clustering model, to assess the uncertainty of the clustering, and to...