Research Area: Data Mining
Streaming large volumes of data has a wide range of real-world applications, e.g., video flows, internet calls, and online games etc. Thus, fast and real-time data stream processing is important. Traditionally, data clustering algorithms are efficient and effective to mine information from large data. However, they are mostly not suitable for online data stream clustering. Therefore, in this work, we propose a novel fast and grid based clustering algorithm for hybrid data stream (FGCH). Specifically, we have made the following main contributions: 1), we develop a non-uniform attenuation model to enhance the resistance to noise; 2), we propose a similarity calculation method for hybrid data, which can calculate the similarity more efficiently and accurately; and 3), we present a novel clustering center fast determination algorithm (CCFD), which can automatically determine the number, center, and radius of clusters. Our technique is compared with several state-of-art clustering algorithms. The experimental results show that our technique can achieve more than better clustering accuracy on average. Meanwhile, the running time is shorter compared with the closest algorithm.
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Author(s) Name: Jinyin Chen, Xiang Lin, Qi Xuan and Yun Xiang
Journal name: Applied Intelligence
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Publisher name: Springer
DOI: 10.1007/s10489-018-1324-x
Volume Information: volume 49, pages 1228–1244 (2019)
