Entropy Minimization is a new clustering algorithm that works with both categorical and numeric data, and scales well to extremely large data sets.

The study, “Using Artificial Intelligence to Determine the Impact of E-Commerce on the Digital Economy,” builds a fused indicator matrix spanning ICT infrastructure, payments, trade and logistics, ...

As for hierarchical clustering, it’s useful when the underlying data has a hierarchical structure as it can often recover the hierarchy. However, it’s less efficient than k-means clustering.

Compared to other clustering techniques, DBSCAN does not require you to explicitly specify how many data clusters to use, explains Dr. James McCaffrey of Microsoft Research in this full-code, ...

In this article we focus on clustering techniques recently proposed for high-dimensional data that incorporate variable selection and extend them to the modeling of data with a known substructure, ...

An important goal in image analysis is to cluster and recognize objects of interest according to the shapes of their boundaries. Clustering such objects faces at least four major challenges including ...