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Network flow and delaminated knowledge library based dynamic file clustering method

A technology of text clustering and network flow, applied in the fields of information processing and network content security

Inactive Publication Date: 2007-10-24
FUDAN UNIV
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Problems solved by technology

The above-mentioned classic clustering algorithms are difficult to meet these two requirements at the same time. Many algorithms must recalculate all n articles for the increase of one article, and the time spent is unbearable

Method used

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  • Network flow and delaminated knowledge library based dynamic file clustering method
  • Network flow and delaminated knowledge library based dynamic file clustering method
  • Network flow and delaminated knowledge library based dynamic file clustering method

Examples

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Embodiment Construction

[0075] The method of the present invention is further described below by way of example.

[0076] In this example, the number of documents to be processed is N=3, the similarity threshold θ=0.5, and the vector dimension of the document category Lc=4, where the text vectors are D1, D2, and D3 respectively, and each word in the vector is followed by The weight information calculated by the article through TF·IDF:

[0077] D1 = {(computer, 0.4), (game, 0.3), (download, 0.3)}

[0078] D2 = {(latest, 0.2), (software, 0.5), (download, 0.3)}

[0079] D3={(Computer, 0.4), (Game, 0.3), (Strategy, 0.3)}

[0080] Then when clustering starts, D1 is processed first, because there is no other category to compare with, so it establishes itself as a new category, which is C1, where C1={(computer, 0.4), (game, 0.3), (download, 0.3)}.

[0081] Then process D2, compare D2 with class C1, build the network flow graph, and only have non-zero edge costs from "(download, 0.3)" in D2 to "(download...

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Abstract

The invention relates to a dynamic text cluster method based on network flow and laminated knowledge base, belonging to information processing and network safety technical field. The inventive method comprises that first assuming the clustered file type is provided with vector character, using TFIDF method to extract and normalize character of single clustered text, using the method that defining meaning distance in the knowledge base to calculate the distance of text and type, adjusting and refreshing keyword and weight of the type of new added file, when present file can not be combined with known types, needs to build new type. And the algorism comprises dynamic character vector extraction, type classification, distance calculation, type combination and new type construction. The invention is characterized in that the cluster process is based on the meaning information provided by the laminated knowledge base but not keyword, the invention can dynamically remove noise data, the similarity is calculated network flow algorism, to confirm optimized match, and the invention can meet real-time refresh cluster of Web text, in particular as non-detect type, without pre-appointing type group.

Description

technical field [0001] The invention belongs to the technical field of information processing and network content security, and in particular relates to a dynamic text clustering method based on network flow and layered knowledge base. technical background [0002] Today we are living in the era of information explosion. According to relevant data, by 2003 the total number of Internet pages in the world reached 13.1 billion. Some experts predict that Chinese will become one of the largest languages ​​on the Internet. The Internet has also become an important channel for people to publish and obtain information. Online media such as news, forums, and blogs have developed into an important window for insight into public opinion in China, and online public opinion is exerting an increasing influence on public thinking and government decision-making. The coverage of China's Internet continues to expand, and Chinese netizens' active speech has reached an unprecedented level. In...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06F17/00G06F17/28
Inventor 闵可锐刘昕刘百祥闫华
Owner FUDAN UNIV
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