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Context-Aware Systems and Applications, and Nature of Computation and Communication. 6th International Conference, ICCASA 2017, and 3rd International Conference, ICTCC 2017, Tam Ky, Vietnam, November 23-24, 2017, Proceedings

Research Article

An Effective of Data Organizing Method Combines with Naïve Bayes for Vietnamese Document Retrieval

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  • @INPROCEEDINGS{10.1007/978-3-319-77818-1_20,
        author={Khanh Bui and Thi Nguyen and Thi Nguyen and Thanh Dao},
        title={An Effective of Data Organizing Method Combines with Na\~{n}ve Bayes for Vietnamese Document Retrieval},
        proceedings={Context-Aware Systems and Applications, and Nature of Computation and Communication. 6th International Conference, ICCASA 2017, and 3rd International Conference, ICTCC 2017, Tam Ky, Vietnam, November 23-24, 2017, Proceedings},
        proceedings_a={ICCASA \& ICTCC},
        year={2018},
        month={3},
        keywords={Support Data mining Text retrieval Information retrieval
     Clustering Document retrieval},
        doi={10.1007/978-3-319-77818-1_20}
    }
    
  • Khanh Bui
    Thi Nguyen
    Thi Nguyen
    Thanh Dao
    Year: 2018
    An Effective of Data Organizing Method Combines with Naïve Bayes for Vietnamese Document Retrieval
    ICCASA & ICTCC
    Springer
    DOI: 10.1007/978-3-319-77818-1_20
Khanh Bui1,*, Thi Nguyen1, Thi Nguyen1,*, Thanh Dao2,*
  • 1: Vietnam Electric Power University
  • 2: Le Quy Don Technical University
*Contact email: linhbk@epu.edu.vn, hantt@epu.edu.vn, tinhdt@mta.edu.vn

Abstract

Data is uploaded to Internet daily that make more and more difficult to mine it. Currently, the available of data mining tools still cannot discover knowledge from data that need semantic with difference dimensions. In this paper we present a method to search the related documents based on clustering that grouped by content. In this, the features are assigned weight by supporting. Experimental results show that the proposed method is really effective, high accuracy and the response results are quickly.

Keywords
Support Data mining Text retrieval Information retrieval Clustering Document retrieval
Published
2018-03-16
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-319-77818-1_20
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