Data clustering is the process of placing data items into groups so that items within a group are similar and items in different groups are dissimilar. The most common technique for clustering numeric ...

k-means clustering: A popular clustering algorithm that partitions data into k clusters by minimising the sum of squared distances between data points and the corresponding cluster centroids.

In this paper, the authors contain a partitional based algorithm for clustering high-dimensional objects in subspaces for iris gene dataset. In high dimensional data, clusters of objects often ...

A k-means-type algorithm is proposed for efficiently clustering data constrained to lie on the surface of a p-dimensional unit sphere, or data that are mean-zero-unit-variance standardized ...

Spectral graph clustering—clustering the vertices of a graph based on their spectral embedding—is commonly approached via K-means (or, more generally, Gaussian mixture model) clustering composed with ...

Common clustering techniques include k-means, Gaussian mixture model, density-based and spectral. This article explains how to implement one version of k-means clustering from scratch using the C# ...