ESA-Stream: Efficient Self-Adaptive Online Data Stream Clustering (Extended Abstract)

Yanni Li, Hui Li, Zhi Wang, Bing Liu, Jiangtao Cui, Hang Fei

Abstract: With ever-increasing data streams from various applications such as smart phones, network monitoring, Internet of Things (IoT), etc., unsupervised clustering of data streams has become an important problem for machine learning and big data analysis. As data streams are data-intensive, temporally ordered, and rapidly evolving, efficiently and effectively online clustering of data streams presents a challenging problem [1] .