Skip to main content

Research on Data Mining Technology of Social Network Associated Information

  • Conference paper
  • First Online:
e-Learning, e-Education, and Online Training (eLEOT 2018)

Abstract

With the popularization of Internet social networking service, the results of association data mining between friend dynamic, microblog and moments that user posting and giving feedback information, which have important influence on government planning, business management and personal affairs decision-making activities. This paper studies the data mining technology of social network related information, analyzes the text data in social network by using the finite state automata (DFSA) and word frequency - reverse file frequency (TF-IDF), and using tree algorithm to sort the data. The simulation results show that this method can realize the classification data mining of social network related information.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Shi, Y., Yuan, Y.: Research on the mode of information dissemination based on social network. Libr. Trib. (06) 220–223 (2009)

    Google Scholar 

  2. Lu, D., Li, S., Xu, C.: DFSA Algorithm for Adaptive Frame Length Adjustment with CHI Tagging. J. Harbin Univ. Sci. Technol. (01), 56–60 (2015)

    Google Scholar 

  3. Yang, S., Wang, J., Dai, B., Li, X., Jiang, Y., Liu, Y.: Research status and prospect of user behavior in online social networks. Bull. Chin. Acad. Sci. (02), 200–215 (2015)

    Google Scholar 

  4. Yao, Q., Ma, H., Yan, H., Chen, Q.: Analysis of individual behavior of social network users from the perspective of psychology. Adv. Psychol. Sci. 22(10), 1647–1659 (2014)

    Article  Google Scholar 

  5. Huang, F., Peng, J., Ning, L.: An evolutionary model of social network views based on information entropy. Acta Phys. Sin. (16), 16–24 (2014)

    Google Scholar 

  6. Li, H., Zhou, Z.: Early warning system of university students’ grade based on data mining. J. Daqing Pet. Inst. (04), 91–95 (2011)

    Google Scholar 

  7. Wu, K.: Machine learning based prediction system for student grading and research. J. Taiyuan Urban Vocat. Techn. Coll. (12), 178–180 (2016)

    Google Scholar 

  8. Wang, Y., Wang, P.: Study on construction of early warning system for college students. Shanghai Educ. Eval. Res. (03), 36–40 (2014)

    Google Scholar 

  9. Lu, D., Ling X.: DFSA algorithm for unequal long time slots in full subgroup. Technol. Meas. Control (09), 55–59 (2013)

    Google Scholar 

  10. Sun, J., Wang, X.: Adaptive fuzzy decision tree algorithm. Comput. Eng. Des. 34(02), 649–653 (2013)

    Google Scholar 

  11. Li, Q., Zhou, X., Wang, L., Zhou, W.: Minimum combination method for mining maximal frequent sets. Appl. Res. Comput. 3(03), 702–704 (2008)

    Google Scholar 

  12. Gao, C., Shen, D., Yu, G., Nie, T., Kou, Y.: A method for mining frequent sets based on uncertain data. Proc. Conf. Natl. Database Churches 82–87 (2008)

    Google Scholar 

  13. Chen, X.: A frequent mining of association rules with constraints. Comput. Eng. Appl. (02), 205–208 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yanxin Jiang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jiang, Y., Mei, X., Sun, G. (2018). Research on Data Mining Technology of Social Network Associated Information. In: Liu, S., Glowatz, M., Zappatore, M., Gao, H., Jia, B., Bucciero, A. (eds) e-Learning, e-Education, and Online Training. eLEOT 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 243. Springer, Cham. https://doi.org/10.1007/978-3-319-93719-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93719-9_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93718-2

  • Online ISBN: 978-3-319-93719-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics