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Steam data are continuous and ubiquitous in nature which can be found in many Web applications operating on Internet. Some instances of stream data are web logs, online users' click-streams, online media streaming and Web transaction records. Stream Mining was proposed as a relatively new data analytic solution for handling such streams. It has been widely acclaimed of its usefulness in real-time...
Business Intelligence (BI) capitalized on data-mining and analytics techniques for discovering trends and reacting to events with quick decisions. We argued that a new breed of data-mining, namely stream-mining where continuous data streams arrive into the system and get mined very quickly, stimulates the design of a new real-time BI architecture. In the past, stream-mining (especially in algorithmic...
Data stream mining algorithm, such as the popular classifier implemented by Hoeffding tree algorithm (HTA) is acclaimed to be able to handle high speed data streams that potentially amounts to infinity. It emerges as a hot research area recently on applying HTA in different applications that require real-time responses and fast decision making. In particular, we discovered the effect of Internet traffic...
Data Stream mining (DSM) is claimed to be the successor of traditional data mining where it is capable of mining continuous incoming data streams in real-time with an acceptable performance. Nowadays many computer applications evolved to online and on-demand basis, fresh data are feeding in at high speeds. Not only a decision response needs to be made rapidly, the trained decision tree models would...
This paper proposes a data mining methodology called Business Intelligence-driven Data Mining (BIdDM). It combines knowledge-driven data mining and method-driven data mining, and fills the gap between business intelligence knowledge and existent various data mining methods in e-Business. BIdDM contains two processes: a construction process of a four-layer framework and a data mining process. A methodology...
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