If your data is completely numeric, then the k-means technique is simple and effective. But if your data contains non-numeric data (also called categorical data) then clustering is surprisingly ...

A collaboration of researchers from the University of California Davis, the National Energy Research Scientific Computing Center, and Intel are working together on the DisCo project to extract insight ...

Apache Spark, the big data processing framework that is a fixture of many Hadoop installs, has reached its 1.4 incarnation. With it comes support for R and Python 3 — two languages in wide use ...

Traditional clustering methods often fail when faced with complex, non-linear data patterns. This is where density-based clustering comes into play.

We introduce a novel statistical procedure for clustering categorical data based on Hamming distance (HD) vectors. The proposed method is conceptually simple and computationally straightforward, ...

Clustering data is the process of grouping items so that items in a group (cluster) are similar and items in different groups are dissimilar. After data has been clustered, the results can be analyzed ...