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)


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.


Data stream mining Classification Preprocessing techniques Hoeffding tree VFDT and CVFDT 


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Vyavasayi Vidya Pratishthan Engineering CollegeRajkotIndia

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