Abstract
In this paper an improved ranked K-medoids algorithm by a specific cell-like P system is proposed which extends the application of membrane computing. First, we use the maximum distance method to choose the initial clustering medoids, maximum distance method which is based on the fact that the farthest initial medoids were the least likely assigned in the same cluster. And then, we realize this algorithm by a specific P system. P system is adequate to solve clustering problem for its high parallelism and lower computational time complexity. By computation of the designed system, one possible clustering result is obtained in a non-deterministic and maximal parallel way. Through example verification, our algorithm can improve the quality of clustering.
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Acknowledgment
This work is supported by the Natural Science Foundation of China (Nos. 61170038, 61472231, 61402187, 61502535, and 61572523).
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Zhang, B., Xiang, L., Liu, X. (2018). An Improved Ranked K-medoids Clustering Algorithm Based on a P System. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2017. Lecture Notes in Computer Science(), vol 10745. Springer, Cham. https://doi.org/10.1007/978-3-319-74521-3_12
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DOI: https://doi.org/10.1007/978-3-319-74521-3_12
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