Abstract
Extracting hot topics from data streams is one of the most exciting tasks that interest the researchers in the social networks field. To achieve that goal, many studies focused on robust data stream clustering algorithms. In this paper, we propose an evolving method to extract hot topics from social networks called SAE-Clus. This work explores a deep learning technique that provides interesting advantages especially in the context of data streams. Our method attempts to meet the principal requirements of data stream clustering algorithms. To evaluate the performance of our proposed methods, experiments were conducted using the “Sanders” and “HCR” datasets.
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Rekik, A., Jamoussi, S. (2017). Deep Learning for Hot Topic Extraction from Social Streams. In: Abraham, A., Haqiq, A., Alimi, A., Mezzour, G., Rokbani, N., Muda, A. (eds) Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). HIS 2016. Advances in Intelligent Systems and Computing, vol 552. Springer, Cham. https://doi.org/10.1007/978-3-319-52941-7_19
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DOI: https://doi.org/10.1007/978-3-319-52941-7_19
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