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Ziniu Yu

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Proceedings Article | 25 May 2023 Paper

Cluster and denoise data with general ratio density

Junliang Wu, Ziniu Yu, William Zhu

Proceedings Volume 12636, 126360M (2023) https://doi.org/10.1117/12.2675242

KEYWORDS: Machine learning, Data mining, Artificial intelligence

Read Abstract +

Density-based clustering is famous for its ability to extract clusters of arbitrary shapes and to detect noise samples, but many existing density-based clustering algorithms suffer from high dimensional or varying density data. To address these issues, we introduce a novel density-based clustering algorithm, general ratio density (GRD). Based on k-NN graph, it combines global density estimation and local density estimation to better detect noise points in varying density situations. During the process of shifting noise points and dividing clusters, our clustering algorithm can cluster and denoise at the same time. Experiment results on real life dataset and synthetic dataset demonstrates the state-of-the-art performance of our algorithm compared to other methods.

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