The Data Science Lab Data Clustering Using a Self-Organizing Map (SOM) with C# Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on technique for visualizing and ...

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.

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, ...

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

A functional clustering (FC) method, k-centres FC, for longitudinal data is proposed. The k-centres FC approach accounts for both the means and the modes of variation differentials between clusters by ...

Multilevel data are structures that consist of multiple units of analysis, one nested within the other. Such data are becoming quite common in political science and provide numerous opportunities for ...