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 ...

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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 ...

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 variable under consideration. Multivariate analysis techniques may be used for several ...