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# ...

The k-means clustering algorithm with k-means++ initialization is relatively simple, easy to implement, and effective. One disadvantage of k-means clustering is that it only works with strictly ...

The k-means algorithm is often used in clustering applications but its usage requires a complete data matrix. Missing data, however, are common in many applications. Mainstream approaches to ...

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 ...

Because of this, k-means clustering can yield different results on different runs of the algorithm — which isn’t ideal in mission-critical domains like finance.