Data stream mining techniques: A review

Eiman Alothali, Hany Alashwal, Saad Harous

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


A plethora of infinite data is generated from the Internet and other information sources. Analyzing this massive data in real-time and extracting valuable knowledge using different mining applications platforms have been an area for research and industry as well. However, data stream mining has different challenges making it different from traditional data mining. Recently, many studies have addressed the concerns on massive data mining problems and proposed several techniques that produce impressive results. In this paper, we review real time clustering and classification mining techniques for data stream. We analyze the characteristics of data stream mining and discuss the challenges and research issues of data steam mining. Finally, we present some of the platforms for data stream mining.

Original languageEnglish
Pages (from-to)728-737
Number of pages10
JournalTelkomnika (Telecommunication Computing Electronics and Control)
Issue number2
Publication statusPublished - Apr 2019


  • Classification
  • Clustering
  • Data stream mining
  • Real-time data mining

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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