Abstract
The fuzzy C-means problem belongs to soft clustering problem, where each given point has relationship to every center point. This problem is different from the k-means problem, where each point should belong to only one cluster. In this paper, we design one seeding algorithm for fuzzy C-means problem and obtain performance ratio \(O(k\mathrm{ln}k)\). We also give the performance guarantee \(O(k^2\mathrm{ln}k)\) of the seeding algorithm based on k-means++ for fuzzy C-means problem. At last, we present our numerical experiment to show the validity of the algorithms.
Supported by Higher Educational Science and Technology Program of Shandong Province (No. J17KA171), Natural Science Foundation of Shandong Province (Nos. ZR2019MA032, ZR2019PA004) of China.
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Liu, Q., Liu, J., Li, M., Zhou, Y. (2020). A Novel Initialization Algorithm for Fuzzy C-means Problem. In: Chen, J., Feng, Q., Xu, J. (eds) Theory and Applications of Models of Computation. TAMC 2020. Lecture Notes in Computer Science(), vol 12337. Springer, Cham. https://doi.org/10.1007/978-3-030-59267-7_19
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