Abstract
Aiming at solving the shortcomings of traditional Single-Pass clustering algorithms, such as low accuracy and large amount of computation, a novel Storm-based parallel Single-Pass clustering algorithm is proposed to discovery of hot events in the food field. In order to solve the problem of data inconsistency in parallel computing, a method of dynamically acquiring cluster increments and random delays is adopted to improve the Single-Pass algorithm. In order to validate the performance of the proposed method, a case study of news events classification is carried out. Simulation results show that the proposed algorithm can effectively improve the cluster repetition in clustering results and greatly improve the accuracy and efficiency of clustering compared with the traditional Single-Pass algorithm.
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Xu, FZ., Jiang, ZY., He, YL., Wang, YJ., Zhu, QX. (2018). A Storm-Based Parallel Clustering Algorithm of Streaming Data. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_12
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DOI: https://doi.org/10.1007/978-3-030-04212-7_12
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