Alignment of cluster complexity at network systems - Enaleev A.K.; Ciganov Vladimir V. (FME Transactions, 2019)
This paper considers data management structures and cluster technologies in large-scale networks. Suboptimal network partitioning problems are formulated on the base of complexity index alignment. We propose methods for these problems solving, in particular the data clusters number and its boundaries determining. We describe a multi-stage iterative scheme for the semantic data mining from a large document with interdependent sections as well. At the first stage, a priori data mining complexity from these sections is estimated. Then we refine this complexity taking into account the revealed data mining from the adjacent sections. Based on this, the final partitioning of the data set of a big document into clusters is formed under circumstances of deadline and restrictions on financial resources. The proposed methods have been applied in some large-scale transport projects.