Abstract
To address the difficulties of “data noise sensitivity” and “cluster center variance” in mainstream clustering algorithms, we propose a novel robust approach for identifying cluster centers unambiguously from data contaminated with noise; it incorporates the strength of homophilic degrees and graph kernel. Exploiting that in-degrees can breed the homophilic distribution if ordered by their associated sorted out-degrees, it is easy to separate clusters from noise. Then we apply the diffusion kernel to the graph formed by clusters so as to obtain graph kernel matrix, which is treated as the measurement of global similarities. Based on local data densities and global similarities, the proposed approach manages to identify cluster centers precisely. Experiments on various synthetic and real-world databases verify the superiority of our algorithm in comparison with state-of-the-art algorithms.
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This work was supported by the grants from China National Natural Science Foundation under Grant No. 613278050 & 61210013.
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Yang, H., Zhao, D., Cao, L., Sun, F. (2016). A Precise and Robust Clustering Approach Using Homophilic Degrees of Graph Kernel. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., Wang, R. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9652. Springer, Cham. https://doi.org/10.1007/978-3-319-31750-2_21
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DOI: https://doi.org/10.1007/978-3-319-31750-2_21
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