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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

PSMA-based PET imaging in newly diagnosed, high-risk localized prostate cancer, a National Cancer Institute (NCI) Cancer Moonshot trial. This is an ASCO Meeting Abstract from the 2025 ASCO ...

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