Abstract
In the clustering algorithms, it is a bottleneck to identify clusters with arbitrarily. In this paper, a new method DAPPFC (density-based affinity propagation for parameter free clustering) is proposed. Firstly, it obtains a group of normalized density from the unsupervised clustering results. Then, the density is used for density clustering for multiple times. Finally, the multiple-density clustering results undergo a two-stage synthesis to achieve the final clustering result. The experiment shows that the proposed method does not require the user’s intervention, and it can also get an accurate clustering result in the presence of arbitrarily shaped clusters with a minimal additional computation cost.
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References
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)
Seife, C.: Big data: the revolution is digitized. Nature 518(7540), 480–481 (2015)
Aggarwal, C.C., Reddy, C.K. (eds.): Data Clustering: Algorithms and Applications. CRC Press, Boca Raton (2013)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, no. 34, pp. 226–231 (1996)
Dueck, D., Frey, B.J.: Non-metric affinity propagation for unsupervised image categorization. In: IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)
Dueck, D., Frey, Brendan, J., Jojic, N., Jojic, V., Giaever, G., Emili, A., Musso, G., Hegele, R.: Constructing treatment portfolios using affinity propagation. In: Vingron, M., Wong, L. (eds.) RECOMB 2008. LNCS, vol. 4955, pp. 360–371. Springer, Heidelberg (2008). doi:10.1007/978-3-540-78839-3_31
Givoni, I.E., Frey, B.J.: Semi-supervised affinity propagation with instance-level constraints. In: AISTATS, pp. 161–168 (2009)
Xu, X.Z., Ding, S.F., Shi, Z.Z., Zhu, H.: Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm. J. Zhejiang University Science C 13(2), 131–138 (2012)
Fujiwara, Y., Irie, G., Kitahara, T.: Fast algorithm for affinity propagation. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, no. 3, p. 2238 (2011)
Wang, K., Zheng, J.: Fast algorithm of affinity propagation clustering under given number of clusters. Comput. Syst. Appl. 7, 207–209 (2010)
Liu, X.Y., Fu, H.: A fast affinity propagation clustering algorithm. Shandong Daxue Xuebao(GongxueBan) 41(4), 20–23 (2011)
Feng, X.L., Yu, H.T.: Research on density-insensitive affinity propagation clustering algorithm. Jisuanji Gongcheng/Computer Engineering, 38(2) (2012)
Chen, X., Liu, W., Qiu, H., Lai, J.: APSCAN: a parameter free algorithm for clustering. Pattern Recogn. Lett. 32, 973–986 (2011)
Frey, B.J., Dueck, D.: Clustering by passing message between data points. Science 315, 972–976 (2007)
Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. ACM Trans. Knowl. Discov. Data 1(1), 1–30 (2007)
Zahn, C.T.: Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans. Comput. 100(1), 68–86 (1971)
Veenman, C.J., Reinders, M.J.T., Backer, E.: A maximum variance cluster algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1273–1280 (2002)
Chang, H., Yeung, D.Y.: Robust path-based spectral clustering. Pattern Recogn. 41(1), 191–203 (2008)
Acknowledgements
This work was supported by National Natural Science Fund of China (61472039), National Key Research and Development Plan of China (2016YFC0803000, 2016YFB0502604), and Specialized Research Fund for the Doctoral Program of Higher Education (20121101110036).
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Yuan, H., Wang, S., Yu, Y., Zhong, M. (2016). DAPPFC: Density-Based Affinity Propagation for Parameter Free Clustering. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science(), vol 10086. Springer, Cham. https://doi.org/10.1007/978-3-319-49586-6_34
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DOI: https://doi.org/10.1007/978-3-319-49586-6_34
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