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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Han, J., Kambr, M.: Data mining concepts and techniques, Elsevier Inc., USA (2012). ch. 8
Razavi Zadegan, S.M., Mirzaie, M., Sadoughi, F.: Ranked k-medoids: a fast and accurate rank-based partitioning algorithm for clustering large datasets. Knowl.-Based Syst. 39(2), 133–143 (2013)
Zhang, T.: BIRCH: an efficient data clustering method for very large databases. ACM SIGMOD Record 25(2), 103–114 (1996)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis (1991)
Sander, J., Ester, M., Kriegel, H.P., et al.: Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Min. Knowl. Disc. 2(2), 169–194 (1998)
Fraley, C., Raftery, A.E.: Model-based clustering, discriminant analysis, and density estimation. J. Am. Stat. Assoc. 97(458), 611–631 (2002)
Wang, W., Yang, J., Muntz, R.R.: STING: a statistical information grid approach to spatial data mining. In: International Conference on Very Large Data Bases, pp. 186–195. Morgan Kaufmann Publishers Inc. (1997)
Peng, H., Wang, J., Pérez-Jiménez, M.J., et al.: An unsupervised learning algorithm for membrane computing. Inf. Sci. 304(C), 80–91 (2015)
Niu, Y., Xiao, J., Jiang, Y.: Time-free solution to 3-coloring problem using tissue P systems. Chin. J. Electron. 25(3), 407–412 (2016)
Acknowledgment
This work is supported by the Natural Science Foundation of China (Nos. 61170038, 61472231, 61402187, 61502535, and 61572523).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-74521-3_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74520-6
Online ISBN: 978-3-319-74521-3
eBook Packages: Computer ScienceComputer Science (R0)