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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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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