Exemplar-based data stream clustering toward Internet of Things

Dealing with dynamic data stream has become one of the most active research fields for Internet of Things (IoT). Specifically, clustering toward dynamic data stream is a necessary foundation for numerous IoT platforms. In this paper, we focus on dynamic exemplar-based clustering models. In terms of the maximum a priori principle, under the probability framework, we first summarize a unified explanation for two typical exemplar-based clustering models, namely enhanced -expansion move (EEM) and affinity propagation (AP). Then, a new dynamic exemplar-based data stream clustering algorithm called DSC is proposed accordingly. The distinctive merit of the proposed algorithm DSC is that we can simply utilize the framework of EEM algorithm through modifying the definitions of several variables and do not need to design another optimization mechanism. Moreover, algorithm DSC is capable of dealing to two cases of similarities. In contrast to both AP and EEM, our experimental results indicate the power of algorithm DSC for real-world IoT data streams.