Abstract
Clustering is an important tool for data mining and analysis for massive data in big data. This paper proposes a clustering model of high-dimensional data based on the density peak cluster algorithm and accomplishes clustering for more than six-dimensional data with arbitrary shape simply and directly. This model achieves automatically pre-process and takes local points with larger density and far away from other local points as the clustering center followed by introducing the fine-tuning. Experimental results suggest that our model not only works for low-dimensional data, but also achieves promising performance for high-dimensional data.
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References
Li, C.P., Li, J.H., He, M.: Concept lattice compression in incomplete contexts based on K-medoids clustering. Intl. J. Mach. Learn. Cybern. 7(4), 539–552 (2016)
Gagolewski, M., Bartoszuk, M., Cena, A.: Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm. Inf. Sci. 363(3), 8–23 (2016)
Ying, W., Chung, F.-L., Wang, S.: Scaling up synchronization-inspired partitioning clustering. IEEE Trans. Knowl. Data Eng. 26(8), 2045–2057 (2014)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
Pereira, R., Fagundes, A., Melicio, R.: A fuzzy clustering approach to a demand response model. Intl. J. Electrical Power Energy Syst. 81(10), 184–192 (2016)
Mahesh Kumar, K., Rama Mohan Reddy, A.: A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method. Pattern Recogn. 58(1), 39–48 (2016)
Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: ordering points to identify the clustering structure. ACM Sigmod Rec. 28(2), 49–60. ACM (1999)
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)
Fu, L., Medico, E.: FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinform. 8(1), 3 (2007)
Maza, S., Simon, C., Boukhobza, T.: Impact of the actuator failures on the structural controllability of linear systems: a graph theoretical approach. IET Control Theory Appl. 6(3), 412–419 (2012)
Chang, H., Yeung, D.Y.: Robust path-based spectral clustering. Pattern Recogn. 41(1), 191–203 (2008)
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Jin, C., Xie, X., Hu, F. (2016). A Density Peak Cluster Model of High-Dimensional Data. In: Wang, G., Han, Y., MartÃnez Pérez, G. (eds) Advances in Services Computing. APSCC 2016. Lecture Notes in Computer Science(), vol 10065. Springer, Cham. https://doi.org/10.1007/978-3-319-49178-3_17
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DOI: https://doi.org/10.1007/978-3-319-49178-3_17
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