Modern data rarely arrives in tidy, well-separated groups. Images, gene expression profiles, sensor streams, and document ...

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 ...

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 ...

Clustering is a data science technique in machine learning that groups similar rows in a data set. After running a clustering technique, a new column appears in the data set to indicate the group each ...

A distributed computing framework can group large volumes of English text more accurately and efficiently than older methods, ...

Compared to other clustering techniques, DBSCAN does not require you to explicitly specify how many data clusters to use, explains Dr. James McCaffrey of Microsoft Research in this full-code, ...

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 ...

Conventional clustering techniques often focus on basic features like crystal structure and elemental composition, neglecting target properties such as band gaps and dielectric constants. A new study ...