Clustering for Context Inference in the Data Stream Mining

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Provided by: Science & Engineering Research Support soCiety (SERSC)
Topic: Data Management
Format: PDF
In analyzing continuous variable data massively flowing in, data clustering helps classify and identify data in which various events. In an environment in which several events are sensed in a complex manner and sequentially obtained, a clue can be obtained for inference of situations by classifying each event and analyzing the aspect of change of each event. This paper proposes a method to efficiently decide the cluster centers in each subsequent time slot for efficient classification of events and inference of situations in a data stream environment.
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