Construction Method And Application Of Complex Network Based On Fuzzy Clustering

Complex systems have dual characteristics for order and chaos,and also widely exist in human society and nature. The complex network can describe complex systems easily. The key point for constructing a proper network generation mechanism and network model is depended on the system’s own characteristics. The research for complex networks will contribute to a breakthrough in other disciplines.This paper proposes a construction method of complex network based on improved fuzzy clustering algorithm. First, to execute cluster analysis on the original data. so we can gather the data of same characteristics into one class which are based on certain standard, and thus formally "reduce" the size of the original data, and improve the efficiency of data processing. Then, we can consider each clustering result as the node of the complex network. The nodes connect together to form the complex network, and then evolution of the network. Finally, getting the ideal complex network according to gradually optimizing the topological structures of complex network.Applying the construction method of complex network based on fuzzy clustering algorithm in this paper, Getting an effective optimal path on the TSP through a simulation experiment. Also constructing the ideal complex network model for the landscape ecological problem. To react the landscape ecological spatial pattern through the topological structure of the network. It provides real and credible basis of tourism spatial resources ranking for Wuyuan country, and improves the qualitative understanding for landscape pattern. It also provides the optimal travel route for developed scenic spots from the perspective of landscape pattern.The fuzzy clustering method can gather data into one class which have the same features, and it can also reflect the fuzziness of the objective world better. On this basis, we can do evolutionary operation on the constructed complex network. After the following operation the complex network model can reflect the actual system real.