Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A single type of machine learning algorithm can be used to identify fake ...

Unsupervised clustering is widely applied in single-cell RNA-sequencing (scRNA-seq) workflows. The goal is to detect distinct cell populations that can be annotated as known cell types or discovered ...

Cluster analysis is one of the most important tools for empirical data analysis. The intuitiveness of the goal of a cluster analysis–grouping objects which are similar–is the reason why researchers ...

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 ...

Thanks to technological advances, scientists have access to vast amounts of data, but in order to put it to work and draw conclusions, they need to be able to process it. In research recently ...

Spectral clustering is quite complex, but it can reveal patterns in data that aren't revealed by other clustering techniques. Data clustering is the process of grouping data items so that similar ...

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

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 ...