The Application of Graph Model in the Cultural Archives

Ping Sun

doi:10.4028/www.scientific.net/AMR.989-994.1779

Paper Titles

Develop Local Fuzzy Classifier to Modify Low-Confidence Output of Global Classifier
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The Improved Algorithm about Binocular Vision of Flexible Structure in On-Orbit Modal Identification
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An Algorithm of Uncertain Reasoning Considering Subjective Factors
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A Fuzzy Clustering Algorithm of Web Customers Based on Attributes Reduction
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The Application of Graph Model in the Cultural Archives
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Deobfuscate Non-Returning Calls and Call-Stack Tampering in Instruction Traces
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Constructing Attractors via the Improved Eugenics Genetic Algorithm
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Comparison Results between Jacobi and USSOR Iterative Methods
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Convergence Results of the Improving Modified Gauss-Seidel (IMGS) Iterative Method for a Symmetric Positive Definite Matrix
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994The Application of Graph Model in the Cultural...

The Application of Graph Model in the Cultural Archives

Abstract:

This article describes a standard document similarity measure, and is used for cultural archives category. And should be used in the classification of cultural archives. the results show that, based on graph model of this representation is valid and feasible.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

1779-1781

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.1779

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Online since:

July 2014

Authors:

Ping Sun*

Keywords:

Cultural Archives, Graph Model, Similarity Measure

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* - Corresponding Author

References

[1] Zou Jia Qi, Chinese document classification based on graph model [j]. small micro-computer system. 2006(4): 754-757.

Google Scholar

[2] W. Lam and F. Bacchus. Learning Bayesian belief networks: An approach Based on the MDL principle. Computational Intelligence, 10(4): 269-293, (1994).

DOI: 10.1111/j.1467-8640.1994.tb00166.x

Google Scholar

[3] Sun Lihua, etc. An improved KNN method and its application in the classification of the document [j]. Applied science and technology. 2002，29( 2) : 25-27.

Google Scholar

[4] WANG Jian-hui, SHEN Zhan, HU Yun-fa. An applicable and efficient clustering algorithm[J]. Journal of Software, 2004, 15(5) : 697-705.

Google Scholar

[5] Slonim N，Tishby N． Document Clustering using Word Clusters via the Information Bottleneck Method［C］/ / Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval，New York，USA，2000: 208-215.

DOI: 10.1145/345508.345578

Google Scholar