期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2019
卷号：17
期号：2
页码：728-737
DOI：10.12928/telkomnika.v17i2.11752
出版社：Universitas Ahmad Dahlan
摘要：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.
其他摘要：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.
关键词：classification;clustering;data stream mining;real-time data mining
