Advertisement

Creation of Data Classification System for Local Administration

  • Raissa Uskenbayeva
  • Aiman MoldagulovaEmail author
  • Nurzhan K. Mukazhanov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)

Abstract

This paper deals with classification of flow of messages coming to the local government from various data sources, such as social networks, website of the government, emails, etc. The primary data, which is extracted from various sources, is stored in the NoSQL database. Further, using special methods and developed applications, the data is classified and sent to the relevant departments. The article focuses on the review of methods and the construction of the architecture of the system of data classification which retrieved from social networks, website of the local government, emails, etc.

Keywords

Data Database Structured data Unstructured data Classification 

Notes

Acknowledgment

This work has been done in the framework of the grant given by Ministry of Education and Science of the Republic of Kazakhstan (Grant No. 0218PК01178).

References

  1. 1.
    Tran, L.Q., Moon, C.W., Le, D.X., Thoma, G.R.: Web page downloading and classification. In: Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001, pp. 321–326 (2001).  https://doi.org/10.1109/cbms.2001.941739
  2. 2.
    Amato, F., Boselli, R., Cesarini, M., Mercorio, F., Mezzanzanica, M., Moscato, V., Picariello, A.: Challenge: processing web texts for classifying job offers. In: Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015, pp. 460–463 (2015).  https://doi.org/10.1109/icosc.2015.7050852
  3. 3.
    Bijalwan, V., Kumar, V., Kumari, P., Pascual, J.: KNN based machine learning approach for text and document mining 7(1), 61–70 (2014)Google Scholar
  4. 4.
    Elden, L.: Matrix Methods in Data Mining and Pattern Recognition. SIAM, Philadelphia, PA, 224 pp. (2007). ISBN 978-0-898716-26-9Google Scholar
  5. 5.
    Hassanat, A.B., Abbadi, M.A., Alhasanat, A.A.: Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach. Int. J. Comput. Sci. Inf. Secur. (IJCSIS) 12(8), 33–39 (2014).  https://doi.org/10.1007/s00500-005-0503-yCrossRefGoogle Scholar
  6. 6.
    Dong, T., Cheng, W.: The research of kNN text categorization algorithm based on eager learning, (d), pp. 1120–1123 (2012).  https://doi.org/10.1109/icicee.2012.297
  7. 7.
    Guo, G., Ping, X., Chen, G.: A fast document classification algorithm based on improved KNN, pp. 3–6 (2006)Google Scholar
  8. 8.
    Pratama, B.Y., Sarno, R.: Personality classification based on Twitter text using Naive Bayes, KNN and SVM. In: 2015 International Conference on Data and Software Engineering (ICoDSE), pp. 170–174 (2015).  https://doi.org/10.1109/icodse.2015.7436992
  9. 9.
    Yan, Z.: Combining KNN algorithm and other classifiers, (1), 1–6 (2010)Google Scholar
  10. 10.
    Shimodaira, H.: Text classification using Naive Bayes, (4) (2015)Google Scholar
  11. 11.
    Wang, L., Zhao, X.: Improved KNN classification algorithms research in text categorization, i, pp. 1848–1852 (2012)Google Scholar
  12. 12.
    Tjandra, S., Alexandra, A., Warsito, P.: Determining citizen complaints to the appropriate government departments using KNN algorithm, pp. 2–5 (2015)Google Scholar
  13. 13.
    Nikhath, A.K., Subrahmanyam, K., Vasavi, R.: Building a K-nearest neighbor classifier for text categorization 7(1), 254–256 (2016)Google Scholar
  14. 14.
    Yunliang, Z., Lijun, Z., Xiaodong, Q., Quan, Z.: Flexible KNN algorithm for text categorization by authorship based on features of lingual conceptual expression, pp. 601–605 (2009).  https://doi.org/10.1109/csie.2009.363
  15. 15.
    Zhou, L., Wang, L.: A Clustering-based KNN improved algorithm CLKNN for text classification, pp. 4–7 (2010)Google Scholar
  16. 16.
    Wang, Y.U., Wang, Z.: A fast KNN algorithm for text categorization, 19–22 Aug 2007Google Scholar
  17. 17.
    Barsegiyan, A.A.: Tekhnologii analiza dannykh: Data Mining, Visual Mining, Text Mining, OLAP / A.A. Barsegyan, M.S. Kupriyanov, V.V. Stepanenko, I.I. Kholod – 2-ye izd., pererab. i dop. – SPb.: BKHV-Peterburg, 384 p. (2007)Google Scholar
  18. 18.
    Moldagulova, A.N., Sulaiman, R.B.: Document classification based on KNN algorithm by term vector space reduction. In: Proceedings of 18th International Conference on Control, Automation and Systems (ICCAS) (2018)Google Scholar
  19. 19.
    Moldagulova, A.N., Sulaiman, R.B.: Using KNN algorithm for classification of textual documents. In: Proceedings of 8th International Conference on Information Technology (ICIT) (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Raissa Uskenbayeva
    • 1
  • Aiman Moldagulova
    • 1
    Email author
  • Nurzhan K. Mukazhanov
    • 1
  1. 1.International Information Technology UniversityAlmatyKazakhstan

Personalised recommendations