Ensemble clustering methods combine multiple clustering results to yield a consensus partition that is often more robust, accurate and stable than any single clustering solution. These techniques ...

A good way to see where this article is headed is to take a look at the screenshot in Figure 1 and the graph in Figure 2. The demo program begins by loading a tiny 10-item dataset into memory. The ...

Clustering non-numeric -- or categorial -- data is surprisingly difficult, but it's explained here by resident data scientist Dr. James McCaffrey of Microsoft Research, who provides all the code you ...

Multivariate analysis in statistics is a set of useful methods for analyzing data when there are more than one variables under consideration. Multivariate analysis techniques may be used for several ...

Researchers have developed a new AI algorithm, called Torque Clustering, that is much closer to natural intelligence than current methods. It significantly improves how AI systems learn and uncover ...

In materials science, substances are often classified based on defining factors such as their elemental composition or crystalline structure. This classification is crucial for advances in materials ...

This is a preview. Log in through your library . Abstract Scott and Knott (1974) have used cluster analysis methods to group means in the analysis of variance. We consider an analogous ...

This is a preview. Log in through your library . Abstract Cluster analysis (CA) has been applied to geophysical research for over two decades although its popularity has increased dramatically over ...