Advertisement

Comparative Study of Various Decision Tree Methods for Data Stream Mining

  • Vaishali MehtaEmail author
  • Vishakha SanghaviEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 797)

Abstract

Nowadays, many physical world appliances found data streams like telecommunication system, multimedia data, medical data streams. Traditional data stream mining allows storage of data and multiple scan of dataset. But it is next to impossible to save or scan it more than one or two times, because of its mountainous size. It is essential to develop the processing systems which scans once and examines the methods. Because of this, data stream mining becomes an emerging topic for research in knowledge discovery. Effective classification of such data streams finds many stream mining provocations like immeasurable length, increment learning, concept drift. So, we have to either update existing mining classifiers or generate a new technique for data stream classification. In this paper, we point out three different classification methods of decision tree called Hoeffding tree, VFDT, and CVFDT, which focuses on these classification problems.

Keywords

Data stream mining Classification Preprocessing techniques Hoeffding tree VFDT and CVFDT 

References

  1. 1.
    Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. Morgan kaufmann publishers, ElsevierzbMATHGoogle Scholar
  2. 2.
    Aggarwal C (2006) Data streams models and algorithms, advances in database systems. Springer VerlagGoogle Scholar
  3. 3.
    Kotecha R, Garg S (2015) Data streams and privacy: two emerging issues in data classification. In: 5th Nirma university international conference on engineering (NUiCONE) 2015. IEEE conference publication, pp 1–6Google Scholar
  4. 4.
    Abdulsalam H, Skillicorn B, Martin P (2011) Classification using streaming random forests. IEEE Trans Knowl Data Eng 23:22–36 (IEEE journals and magazines)CrossRefGoogle Scholar
  5. 5.
    Mao G, Yang Y (2011) A micro-cluster based ensemble approach for classifying distributed data streams. In: 23rd IEEE International conference on tools with artificial intelligence 2011. IEEE conference publication, pp 753–759Google Scholar
  6. 6.
    Kirkby R (2007) Improving Hoeffding Trees. Ph.D. thesis, Department of Computer Science, University of WaikatoGoogle Scholar
  7. 7.
    Masud M, Gao J, Khan L, Han J, Thuraisingham B (2011) Classification and novel class detection in concept-drifting data streams under time constraints. IEEE Trans Knowl Data Eng 23:859–874 (IEEE journals and magazines)CrossRefGoogle Scholar
  8. 8.
    Kholghi M, Keyvanpour M (2011) An analytical framework for data stream mining techniques based on challenges and requirements. Int J Eng Sci Technol (IJEST) 3:1–7Google Scholar
  9. 9.
    Aggarwal C, Han J, Wang J, Yu P (2006) A framework for on-demand classification of evolving data streams. IEEE Trans Knowl Data Eng 18:577–589 (IEEE journals and magazines)CrossRefGoogle Scholar
  10. 10.
    Shukla M, Rathod K (2013) Stream data mining and comparative study of classification algorithms. Int J Eng Res Appl 3(1):163–168Google Scholar
  11. 11.
    Domingos P, Hulten G (2001) Mining high-speed data streams. In: International conference on knowledge discovery and data miningGoogle Scholar
  12. 12.
    Domingos P, Spencer L, Hulten G (2001) Mining time-changing data streams. In: 7th ACM SIGKDD international conference on knowledge discovery and data miningGoogle Scholar
  13. 13.
    Raahemi1 B, Zhong W, Liu J (2008) Peer-to-Peer traffic identification by mining IP layer data streams using concept-adapting very fast decision tree. In: 20th IEEE International conference on tools with artificial intelligence 2008, vol 1. IEEE conference publication, pp 525–532Google Scholar
  14. 14.
    Thakong M, Phimoltares S, Jaiyen S, Lursinsap C (2017) Fast learning and testing for imbalanced multi-class changes in streaming data by dynamic multi-stratum network. IEEE Access 5:10633–10648 (IEEE journals and magazines)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Vyavasayi Vidya Pratishthan Engineering CollegeRajkotIndia

Personalised recommendations