Abstract
Data clustering, as one of the major algorithms in facial feature data mining, its efficiency and quality plays a key role. DBSCAN is a classic density-based clustering algorithm. It is applicable to any shape of subset or cluster and is comparatively anti-noise, but because of its slow running speed, it has long convergence time when the data set is large. For this reason, the paper presents an improved DBSCAN algorithm based on distributed computing system, by making full use of the characteristic of high time partial similarity of facial feature under the monitoring scene, first to merge the features which belong to the same target, then to segment the merged result using DBSCAN algorithm. The test results show that although this new algorithm has no modification in the aspect of time complexity, but greatly improved clustering speed, which enables the system to process million-level datasets.
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Lin, Q., Zhuo, B., Jiao, L., Liao, L., Guo, J. (2021). Distributed Facial Feature Clustering Algorithm Based on Spatiotemporal Locality. In: Barolli, L., Poniszewska-Maranda, A., Park, H. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2020. Advances in Intelligent Systems and Computing, vol 1195. Springer, Cham. https://doi.org/10.1007/978-3-030-50399-4_38
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DOI: https://doi.org/10.1007/978-3-030-50399-4_38
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