In recent years, machine-learning methods have become increasingly important for the experiments at Large Hadron Collider (LHC). They are utilised in everything from trigger systems to reconstruction and data analysis. The UCluster method is a general model providing unsupervised clustering of particle physics data, that can be easily modified provide solutions variety different decision proble...

Matrix decomposition is one of the fundamental tools to discover knowledge from big data generated by modern applications. However, it still inefficient or infeasible process very using such a method in single machine. Moreover, are often distributedly collected and stored on different machines. Thus, generally bear strong heterogeneous noise. It essential useful develop distributed matrix for ...

In this article, a coalitional robust model predictive controller for tracking target sets is presented. The overall system controlled by set of local control agents that dynamically merge into cooperative coalitions or clusters so as to attain an efficient tradeoff between cooperation burden and global performance optimality. Within each cluster, the coordinate their inputs maximize collective...

Many distributed data mining DDM tasks such as distributed association rules and distributed classification have been proposed and developed in the last few years. However, only a few research concerns distributed clustering for analysing large, heterogeneous and distributed datasets. This is especially true with distributed density-based clustering although the centralised versions of the tech...