Abstract
Sensor-based monitoring systems are growing enormously which lead to generation of real-time sensor data to a great extent. The classification and clustering of this data are a challenging task within the limited memory and time constraints. The overall distribution of data is changing over the time, which makes the task even more difficult. This paper proposes a dynamic clustering algorithm to find and detect the different contexts in a sensor-based system. It mines dynamically changing sensor streams for different contexts of the system. It can be used for detecting the current context as well as in predicting the coming context of a sensor-based system. The algorithm is able to find context states of different length in an online and unsupervised manner which plays a vital role in identifying the behavior of sensor-based system. The experiments results on real-world high-dimensional datasets justify the effectiveness of the proposed clustering algorithm. Further, discussion on how the proposed clustering algorithm works in sensor-based system is provided which will be helpful for domain experts.
Published Version
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