Co-clustering algorithms and models represent a robust framework for the simultaneous partitioning of the rows and columns in a data matrix. This dual clustering approach, often termed block ...

Facility location and clustering algorithms constitute a critical area of research that bridges optimisation theory and data analysis. Facility location techniques focus on the strategic placement of ...

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 ...

Clustering algorithms are used to generate clusters of elements having similar characteristics. Among the different groups of clustering algorithms, agglomerative algorithm is widely used in the ...

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 ...

A good way to see where this article is headed is to take a look at the screenshot in Figure 1 and the graph in Figure 2. The demo program begins by loading a tiny 10-item dataset into memory. The ...

About every 10 minutes, it seems, a new article about a "revolutionary breakthrough" in AI hits my screen. A new approach, a new feature, billions of dollars this, AI agents that. It has been non-stop ...

Artificial intelligence comes from machines - and precisely for that reason, it isn't automatically fair. Doctoral researcher ...