Abstract
Clustering is the unsupervised classification of patterns into groups. In this paper, a clustering algorithm for weighted similarity graph is proposed based on minimum and normalized cut. The minimum cut is used as the stopping condition of the recursive algorithm, and the normalized cut is used to partition a graph into two subgraphs. The algorithm has the advantage of many existing algorithms: nonparametric clustering method, low polynomial complexity, and the provable properties. The algorithm is applied to image segmentation; the provable properties together with experimental results demonstrate that the algorithm performs well.
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Wang, J., Peng, H., Hu, J., Yang, C. (2007). A Graph Clustering Algorithm Based on Minimum and Normalized Cut. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2007. ICCS 2007. Lecture Notes in Computer Science, vol 4487. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72584-8_66
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DOI: https://doi.org/10.1007/978-3-540-72584-8_66
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