Abstract
Support vector clustering is a nonparametric and unsupervised clustering algorithm based on nonlinear mapping. The algorithm obtains the set of isolines containing data through mapping and reflection operations. In order to complete the clustering, we need to deal with the set of equal value lines. This paper studies the cluster labeling (CL) method based on path sampling, including complete graph (CG) method, support vector graph (SVG) method, similarity graph (PG) method, and gradient descent (GD) method.
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Acknowledgements
This paper was supported by the National Natural Science Foundation of China partly under the Grant no. 61601499, 61701527, and 61601503, by Natural Science Foundation (2020JM-345, 2020JQ-482, 2019JM-155) of Shaanxi Province.
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Shiqiang, W., Caiyun, G., Huiyong, Z., Juan, B., Binfeng, Z., Jiliang, C. (2021). Research on Clustering Identification Method Based on Path Sampling in Support Vector Clustering. In: WU, C.H., PATNAIK, S., POPENTIU VLÃDICESCU, F., NAKAMATSU, K. (eds) Recent Developments in Intelligent Computing, Communication and Devices. ICCD 2019. Advances in Intelligent Systems and Computing, vol 1185. Springer, Singapore. https://doi.org/10.1007/978-981-15-5887-0_28
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DOI: https://doi.org/10.1007/978-981-15-5887-0_28
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