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 ...

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 ...

Entropy Minimization is a new clustering algorithm that works with both categorical and numeric data, and scales well to extremely large data sets. Data clustering is the process of placing data items ...

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 ...

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 Cluster analysis (CA) has been applied to geophysical research for over two decades although its popularity has increased dramatically over ...

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 ...