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

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...

k-medoids is a classical partitioning technique of clustering that splits the data set of n objects into k clusters, where the number k of clusters assumed...

K-medians clustering is a partitioning technique used in cluster analysis. It groups data into k clusters by minimizing the sum of distances—typically...

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...

purpose of K-means clustering is to classify data based on similar expression. K-means clustering algorithm and some of its variants (including k-medoids)...

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

These clusters then define segments within the image. Here are the most commonly used clustering algorithms for image segmentation: K-means Clustering: One...

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

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

K-means clustering is an approach for vector quantization. In particular, given a set of n vectors, k-means clustering groups them into k clusters (i...

co-authored highly cited research papers on nearest neighbor search and k-means clustering. He has published many papers on computer chess, was the local organizer...

results. It has been asserted that the relaxed solution of k-means clustering, specified by the cluster indicators, is given by the principal components, and...

Automatic clustering algorithms are algorithms that can perform clustering without prior knowledge of data sets. In contrast with other cluster analysis...

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...

similarities between data points, such as clustering and similarity search. As an example, the K-means clustering algorithm is sensitive to feature scales...

singular value decomposition approach. k-SVD is a generalization of the k-means clustering method, and it works by iteratively alternating between sparse coding...

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...

computer science, constrained clustering is a class of semi-supervised learning algorithms. Typically, constrained clustering incorporates either a set of...

Look up clustering in Wiktionary, the free dictionary. Clustering can refer to the following: In computing: Computer cluster, the technique of linking...

correlation analysis (CCA) techniques as a pre-processing step, followed by clustering by k-NN on feature vectors in reduced-dimension space. This process is also...

Consensus clustering is a method of aggregating (potentially conflicting) results from multiple clustering algorithms. Also called cluster ensembles or...

transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each...

the minimization of K-means clustering. Furthermore, the computed H {\displaystyle H} gives the cluster membership, i.e., if H k j > H i j {\displaystyle...

diagram Rate-distortion function Data clustering Centroidal Voronoi tessellation Image segmentation K-means clustering Autoencoder Deep Learning Part of this...

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

transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each...

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

accelerate spherical k-means clustering the same way the Euclidean triangle inequality has been used to accelerate regular k-means. A soft cosine or ("soft"...