ESA-Stream: Efficient Self-Adaptive Online Data Stream Clustering

Many big data applications produce a massive amount of high-dimensional, real-time, and evolving streaming data. Clustering such data streams with both effectiveness and efficiency are critical for these applications. Although there are well-known data stream clustering algorithms that are based on the popular online-offline framework, these algorithms still face some major challenges. Several critical questions are still not answer satisfactorily: How to perform dimensionality reduction effectively and efficiently in the online dynamic environment? How to enable the clustering algorithm to achieve complete real-time online processing? How to make algorithm parameters learn in a self-supervised or self-adaptive manner to cope with high-speed evolving streams? In this paper, we focus on tackling these challenges by proposing a fully online data stream clustering algorithm (called ESA-Stream) that can learn parameters online dynamically in a self-adaptive manner, speedup dimensionality reduction, and cluster data streams effectively and efficiently in an online and dynamic environment. Experiments on a wide range of synthetic and real-world data streams show that ESA-Stream outperforms state-of-the-art baselines considerably in both effectiveness and efficiency.

Automated summary

This paper proposes a fully online data stream clustering algorithm called ESA-Stream, which can dynamically learn parameters in a self-adaptive manner, speed up dimensionality reduction, and effectively and efficiently cluster data streams in an online and dynamic environment. Experimental results on a wide range of synthetic and real-world data streams show that ESA-Stream outperforms state-of-the-art baselines considerably in both effectiveness and efficiency.