DOI QR코드

DOI QR Code

Analysis of Clustering Uncertain Data and Uncertain Data Stream

  • Received : 2024.10.05
  • Published : 2024.10.30

Abstract

The problem of handling uncertain data has been attracting the attention of researchers. This paper mainly focuses on uncertain data clustering and noise data streams. Therefore, we will provide a framework to realize the effect of uncertainty. Nowadays, a large number of tenders are present which measure the data roughly. As, sensors normally have distortion in their results cause of the imprecisions transmission in data and retrieval. Mostly these errors are identified. This information is used for minimizing process to advance results according to quality. In this paper, we compare general methods of monitor uncertainty, which have described in the different research papers.

Keywords

References

  1. C. C. Aggarwal and P. S. Yu, "A Framework for Clustering Uncertain Data Stream," in IEEE 24th International Conference on Data Engineering, Cancun, Mexico, 2008. 
  2. Z. V. Bahnemiry, M. M. Pedram and M. Mirzarezaee, "Clustering Uncertain Graph Data Stream," AppliedMathematics& Information Sciences Letters, pp. 85-96, 2016. 
  3. S. Ding, J. Zhang, H. Jia and J. Qian, "An Adaptive Density Data Stream Clustering Algorithm," Cognitive Computation, vol. 8, no. 1, pp. 30-38, 2016. 
  4. J. Ren, S. D. Lee, X. Chen, B. Kao, R. Cheng and D. Cheung, "Naive Bayes Classification of Uncertain Data," in 2009 Ninth IEEE International Conference on Data Mining, Miami, FL, USA, 2009. 
  5. M. Khalilian, N. Mustapha and N. Sulaiman, "Data stream clustering by divide and conquer approach based on vector model," Journal of Big Data, vol. 3, no. 1, p. 21, 2016. 
  6. E. Schubert, A. Koos, T. Emrich, A. Zufle, K. A. Schmid and A. Zimek, "A Framework for Clustering Uncertain Data," in 41st International Conference on Very Large Data Bases, Kohala Coast, 2015. 
  7. C. Zhang , A. Zhou and M. Gao, "Tracking High Quality Clusters over Uncertain Data," in IEEE International Conference on Data Engineering, 2009. 
  8. P. B. Volk, F. Rosenthal, M. Hahmann, D. Habich and W. Lehner, "Clustering Uncertain DataWith PossibleWorlds," in 2009 IEEE 25th International Conference on Data Engineering, Shanghai, China, 2009. 
  9. M. A. Sheela and M. C. Sunitha, "High Dimensional Data & High Speed Data Streams - A Survey," International Journal of Advanced Research in Computer Science, vol. 5, no. 6, p. 3, 2014. 
  10. Y. Xia, B. Qin , S. Prabhakar and Y. Tu, "A Rule-Based Classification Algorithm for Uncertain Data," in 2009 IEEE 25th International Conference on Data Engineering, Shanghai, China, 2009. 
  11. M. Farhan et al., "IoT-based students interaction framework using attention-scoring assessment in eLearning," Futur. Gener. Comput. Syst., 2018. 
  12. M. Farhan, M. Aslam, S. Jabbar, and S. Khalid, "Multimedia based qualitative assessment methodology in eLearning: student teacher engagement analysis," Multimed. Tools Appl., pp. 1-15, 2016. 
  13. M. Farhan, "A methodology to enrich student-teacher interaction in elearning," pp. 185-186, 2015. 
  14. M. Farhan, M. Aslam, S. Jabbar, S. Khalid, and M. Kim, "Real-time imaging-based assessment model for improving teaching performance and student experience in e-learning," J. Real-Time Image Process., vol. 13, no. 3, pp. 491-504, 2017. 
  15. M. M. Iqbal, M. Farhan, Y. Saleem, and M. Aslam, "Automated Web-Bot Implementation using Machine Learning Techniques in eLearning Paradigm," vol. 4, pp. 90-98, 2014. 
  16. H. M. Truong, "Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities," Comput. Human Behav., vol. 55, pp. 1185-1193, 2016. 
  17. M. Farhan et al., "A Real-Time Data Mining Approach for Interaction Analytics Assessment: IoT Based Student Interaction Framework," Int. J. Parallel Program., 2017. 
  18. S. Ahmad, "A New Approach to Multi Agent Based Architecture for Secure and Effective E-learning," vol. 46, no. 22, pp. 26-29, 2012. 
  19. A. Paul, A. Ahmad, M. M. Rathore, and S. Jabbar, "Smartbuddy: Defining human behaviors using big data analytics in social internet of things," IEEE Wirel. Commun., vol. 23, no. 5, pp. 68-74, 2016.