Abstract
The functional k-means problem involves different data from k-means problem, where the functional data is a kind of dynamic data and is generated by continuous processes. By defining a new distance with derivative information, the functional k-means clustering algorithm can be used well for functional k-means problem. In this paper, we mainly investigate the seeding algorithm for functional k-means problem and show that the performance guarantee is obtained as \(8(\mathrm{ln}~k+2)\). Moreover, we present the numerical experiment showing the validity of this algorithm, comparing to the functional k-means clustering algorithm.
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Acknowledgments
The first author is supported by Higher Educational Science and Technology Program of Shandong Province (No. J17KA171). The second author is supported by National Natural Science Foundation of China (No. 61433012), Shenzhen research grant (KQJSCX20180330170311901, JCYJ20180305180840138 and GGFW2017073114031767). The third author is supported by National Natural Science Foundation of China (No. 11531014). The fourth author is supported by National Natural Science Foundation of China (No. 11871081).
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Li, M., Wang, Y., Xu, D., Zhang, D. (2019). The Seeding Algorithm for Functional k-Means Problem. In: Du, DZ., Duan, Z., Tian, C. (eds) Computing and Combinatorics. COCOON 2019. Lecture Notes in Computer Science(), vol 11653. Springer, Cham. https://doi.org/10.1007/978-3-030-26176-4_32
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DOI: https://doi.org/10.1007/978-3-030-26176-4_32
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