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篇名 |
A Fast Clustering Method for Real-Time IoT Data Streams
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並列篇名 | A Fast Clustering Method for Real-Time IoT Data Streams |
作者 | Jing Sun、Xin Yao |
英文摘要 | As an effective way of data analysis, clustering is widely applied in the IoT based applications. By studying the related existing proposals of data clustering, a new clustering method for IoT Data streams is proposed in the present work. Firstly, the characteristics of PML documents in the process of data acquisition and identification are introduced and a hybrid PML document similarity calculation method based on the Bayesian network is developed and expected to assist in data streams clustering. Secondly, a PML data streams clustering method based on a dynamic sliding window is proposed. Finally, we evaluate the performance of our clustering method and the related methods with respect to Running time, Similarity, Purity, Entropy, and F-measure. Experimental results exhibit that the innovative clustering approach can adaptively learn from data streams that change over time, while still maintains comparable accuracy and speed. |
起訖頁 | 083-094 |
關鍵詞 | PML、Bayesian network model、data streams clustering、dynamic sliding window |
刊名 | 電腦學刊 |
期數 | 202102 (32:1期) |
DOI |
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