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...

defined for a single cluster. k-medians is a variation of k-means clustering where instead of calculating the mean for each cluster to determine its centroid...

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...

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

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...

The k-medoids problem is a clustering problem similar to k-means. The name was coined by Leonard Kaufman and Peter J. Rousseeuw with their PAM (Partitioning...

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

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

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)...

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...

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...

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...

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

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

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

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

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

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

K-means clustering problem. The following are some prototype methods K-means clustering Learning vector quantization (LVQ) Gaussian mixtures While K-nearest...

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...

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...

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

fine-tuning. Such schedules have been known since the work of MacQueen on k-means clustering. Practical guidance on choosing the step size in several variants...

identity information. Mixture models are used for clustering, under the name model-based clustering, and also for density estimation. Mixture models should...

Classification Clustering Density-Based Clustering Fuzzy C-Means Clustering Hierarchical Clustering Model-based clustering Neighborhood-based Clustering (i.e., K-Means...