Abstract: With the advance of both cloud computing and internet of things,many applications generate huge amounts of data streams at fast speed.Examples include real-time surveillance systems,vehicle traffic monitoring systems,electricity consumption recording,and network traffic monitoring.Data stream mining has become a hot research topic.Its goal is to extract hidden knowledge/patterns from continuous data streams.Clustering,one of the most important problems in stream mining,has been highly explored.Different from traditional data clustering algorithm where given datasets are generally static and can be repeatedly read and processed,clustering data streams face more challenges due to having to satisfy such constraints as bounded memory,single-pass,real-time response and concept-drift detection.This paper pre-sented a new clustering algorithm for data streams,called EG-Stream,by combining the Euler kernel method with the Growing Neural Gas(GNG) model.It can not only maintain the benefit of nonlinear modeling using kernel function,but also significantly solve the large scale computational problem in kernel-based clustering.Euler kernel is relying on a nonlinear and robust cosine metric that is less sensitive to outliers.More important,it intrinsically induces an empirical map which maps data onto a complex space of the same dimension,and it takes these advantages to measure the similari-ty between data in a robust way without increasing the dimensionality of data,which avoids the problem that other kernel clustering algorithms can not deal with data streams.Although this method is embarrassingly simple just by incorporating the Euler kernel into GNG,the experimental results on variety of UCI datasets indicate that this method can still achieve comparable or even better performance than G-Stream algorithm,and identify the structural information from stream data.

Key words: Data stream clustering, Euler kernel, GNG, Kernel method