Since the emergence of the Zika virus epidemic in 2014 and the associated novel sequalae that emerged, much has been learned about the effects of antenatal exposure to Zika virus. Zika virus in ...

Distributed data mining techniques and mainly distributed clustering are widely used in the last decade because they deal with very large and heterogeneous datasets which cannot be gathered centrally.

There were so many mines on Larisa Sysenko's small farm in Kamyanka in eastern Ukraine after the Russians were pushed out ...

Multi-view clustering (MVC) for remote sensing data has demonstrated significant potential in Earth observation, given its ability to aggregate multi-source information without relying on labels.

This is where data mining becomes essential—offering powerful tools to extract meaningful features, detect hidden structures, and build predictive models.Recent advances in machine learning, deep ...

Data stream clustering algorithms play a pivotal role in the analysis of rapidly accumulating and evolving data sets, particularly as technologies such as the Internet of Things expand and ...

Clustering is the process of using heuristic to link together individual UTXOs that are controlled by a single wallet. It is ...

The application of data mining algorithms in animal weight prediction has emerged as a vital tool for modern livestock management. Advanced techniques such as Classification and Regression Trees ...

A research team from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has proposed a novel model optimization algorithm—External Calibration-Assisted Screening (ECA)— that ...