Crowd information clustering method based on hyper-element heuristic algorithm

A crowd information and heuristic algorithm technology, applied in the field of information processing, can solve problems such as inability to set different algorithms, changes in crowd environment, different clustering patterns, etc., to reduce space complexity, improve clustering ability, and improve clustering Class quality effects
CN110276376AInactive Publication Date: 2019-09-24JIAXING VOCATIONAL TECHN COLLEGE
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
JIAXING VOCATIONAL TECHN COLLEGE
Publication Date
2019-09-24
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention provides a crowd information clustering method based on a hyper-element heuristic algorithm. The crowd information clustering method solves the problem that it is difficult to establish crowd clustering in a dynamic environment in the prior art. The crowd information clustering method comprises the following steps: establishing a social graph model according to a social network of a crowd; obtaining an adjacent matrix A and elements a_{ij} of the matrix according to the model; obtaining the grade of the node i; by analyzing and judging the grade of the node i, determining a strong sensory group and a weak sensory group, so that a clustering target of the crowd information is defined, and the clustering target comprises a clustering target NRA and a clustering target RC; and designing a hyper-element heuristic algorithm to carry out clustering processing on the clustering target. According to the crowd information clustering method, the clustering capability of the algorithm in the dynamic network environment of the crowd information can be effectively improved, and the clustering quality is improved.
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Description

technical field

[0001] The invention belongs to the technical field of information processing, and relates to a crowd information clustering method based on a super-heuristic algorithm. Background technique

[0002] At present, all kinds of social software in our country continue to develop and be widely used, bringing a lot of convenience to the people's life and entertainment. However, the use of social networks to carry out group-style illegal and criminal activities is also more concealed and more destructive, causing hidden dangers to the safety of people's lives and property. Therefore, by clustering the crowd data information in the social network, the personnel structure of different niche groups and their relationship in the social big data network will be effectively mined.

[0003] In the past ten years, various types of network clustering algorithms have emerged, among which the more famous algorithms include Girvan-Newman algorithm, fast greedy module optimizat...

Claims

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