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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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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