Graph clustering method based on attribute fusion

Abstract

The present invention discloses a graph clustering method based on attribute fusion. A multi-layer attribute fusion model is constructed, the attribute characteristics and the structure relation of nodes in a graph are divided into different hierarchies, the data structure relation and the attribute characteristics are uniformly combined to the same bottom layer network for clustering operation, the data structure and attributes are subjected to weight fusion according to the node ballot mechanism in the clustering, and the weight coefficients of an attribute layer are subjected to adaptive changing to allow a final clustering result to reflect the original distribution of data so as to solve the influence problem of the clustering result of the setting of the initial value of the attribute layer and allow the final clustering to reach a better effect.

Description

technical field [0001] The invention belongs to the technical field of data mining, and more specifically relates to a graph clustering method based on attribute fusion. Background technique [0002] With the rapid development of the Internet, a large amount of complex graph-structured data has been generated in related fields. How to make full use of these data and mine useful knowledge and information has become a current research hotspot. Graph clustering technology is an effective method to mine graph structure data, and has important practical application value in the fields of biology, chemistry and social network. [0003] However, traditional graph clustering techniques such as partition-based clustering methods, density-based clustering, hierarchical-based clustering, and model-based clustering often only consider the topological relationship of the graph and the similarity of node attributes, and divide the graph into For a subgraph with a tight structure or a sub...

AI Technical Summary

Problems solved by technology

The algorithm needs to specify the number of clusters in advance, but there may be a large deviation between the specified number of clusters and the real clustering results without prior conditions, making the clustering effect poor

In view of the problems existing in the NetScan algorithm, Moser et al. proposed the JointClust algorithm that does not need to specify the number of clusters in advance. Although it does not need to specify the number of clusters, it still needs to initialize the selection of cluster centers, so that the clustering results and the selection of initial cluster centers have a relationship. great relationship

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