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 multipledensity 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.
CITATION STYLE
Yuan, H., Wang, S., Yu, Y., & Zhong, M. (2016). DAPPFC: Density-based affinity propagation for parameter free clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10086 LNAI, pp. 495–506). Springer Verlag. https://doi.org/10.1007/978-3-319-49586-6_34
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