Use of combination of hierarchical algorithms and evolutionary learning algorithms for Improved the Accuracy of Clustering of Complex Dynamical Networks Nodes

EasyChair Preprint 1668

Abstract

Clustering is used in a variety of areas, including complex dynamic networks. The nodes in a network's forums are likely to share similar interests. Clustering is widely used in the design of recommendation systems. This method first identifies nodes that have previously had similar activity to the active node, then predicts what nodes the active node will like and recommends them based on peer node privileges. Accordingly, in this study, we try to use clustering systems to improve the clustering of dynamic complex network nodes. Our main objective in this research is to improve clustering of nodes of complex dynamic networks using clustering systems. One of the assumptions of this study is that the clustering system can provide an efficient method for clustering nodes of complex dynamic networks, and can also be used as a parameter for clustering them based on spatial and temporal records. To take. Finally, in this paper, we try to improve the use of hierarchical algorithm and evolutionary learning algorithm in clustering nodes in complex dynamic networks.

Keyphrases: Clustering, Dynamic Complex Networks, Evolutionary Learning Algorithm, Hierarchical algorithm

@booklet{EasyChair:1668,
  author    = {Alireza Farhang},
  title     = {Use of combination of hierarchical algorithms and evolutionary learning algorithms for Improved the Accuracy of Clustering of Complex Dynamical Networks Nodes},
  howpublished = {EasyChair Preprint 1668},
  year      = {EasyChair, 2019}}