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Graph clustering method based on gravitation similarity

A similarity and graph clustering technology, applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve the problems of high time complexity and poor clustering effect.

Inactive Publication Date: 2018-11-06
XIAN UNIV OF TECH
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Problems solved by technology

[0004] The purpose of the present invention is to provide a graph clustering method based on gravitational similarity, which solves the problems of poor clustering effect and high time complexity of existing methods

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  • Graph clustering method based on gravitation similarity
  • Graph clustering method based on gravitation similarity
  • Graph clustering method based on gravitation similarity

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Embodiment Construction

[0063] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0064] In the graph clustering method based on gravitational similarity of the present invention, the clustered graph is an undirected weighted graph whose nodes contain classification attributes, which is defined as: an undirected weighted graph whose nodes include classification attributes can use a triplet Indicates that G={V,A,E}, where, V={v 1 ,v 2 ,...,v n} is a non-empty set containing n nodes, each node contains m classification attributes; A={a 1 ,a 2 ,...,a m} is a non-empty collection of m attributes, attribute a j The value range is It is finite and disordered; E={(v i ,v j )|v i ,v j ∈V} is the set of undirected edges, node v i with v j edge weight w ij >0;

[0065] The specific operation process includes the following steps:

[0066] Step 1. Construct the objective function F(W,Z);

[0067] Step 2. Select k node...

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Abstract

The present invention discloses a graph clustering method based on gravitation similarity, and mainly aims to improve a clustering effect of an undirected weighted graph with nodes including a classification attribute. The specific steps include: first constructing an objective function, then selecting k nodes with the largest number of degrees as an initial clustering center to calculate gravitation similarity of each node to a cluster center to update a membership matrix to obtain initial division, calculating an objective function value, then updating the cluster center in each cluster according to a cluster center updating method, and then updating the membership matrix and calculating the objective function value, till the target function value no longer changes, an algorithm stoppingdivision and obtaining a final division result. The graph clustering method based on gravitation similarity disclosed by the invention solves the problem that an existing method does not comprehensively consider the topological structure of a graph, the attributive characters of nodes and relatively high time complexity.

Description

technical field [0001] The invention belongs to the technical field of data mining methods, and relates to a graph clustering method based on gravitational similarity. Background technique [0002] Graph is a very important data structure, it can visually describe the relationship between data objects, so it is often used to describe the topology of complex networks such as human relationship networks, epidemic transmission networks, sensor networks and protein networks . Graph clustering technology can effectively discover the community structure in complex networks, thereby helping researchers better understand the characteristics and functions of complex networks, and predict the evolution of complex networks. The main difference between graph clustering and traditional clustering is that the similarity of nodes in graph clustering not only depends on the topological structure of the graph but also on the similarity of attributes between nodes, while the similarity of da...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23211
Inventor 周红芳张懿辉刘艺彬
Owner XIAN UNIV OF TECH
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