Data stream mining techniques: a review

Eiman Alothali, Hany Alashwal, Saad Harous

Abstract


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.

Keywords


classification; clustering; data stream mining; real-time data mining;

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DOI: http://dx.doi.org/10.12928/telkomnika.v17i2.11752

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TELKOMNIKA Telecommunication, Computing, Electronics and Control
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