@inproceedings{c1b71f9067c443abb2690a67372bf73e,
title = "DAPPFC: Density-based affinity propagation for parameter free clustering",
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 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{\textquoteright}s intervention, and it can also get an accurate clustering result in the presence of arbitrarily shaped clusters with a minimal additional computation cost.",
keywords = "Affinity propagation, Clustering, Density-based clustering, Parameter free",
author = "Hanning Yuan and Shuliang Wang and Yang Yu and Ming Zhong",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 12th International Conference on Advanced Data Mining and Applications, ADMA 2016 ; Conference date: 12-12-2016 Through 15-12-2016",
year = "2016",
doi = "10.1007/978-3-319-49586-6\_34",
language = "English",
isbn = "9783319495859",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "495--506",
editor = "Jianxin Li and Xue Li and Shuliang Wang and Jinyan Li and Sheng, \{Quan Z.\}",
booktitle = "Advanced Data Mining and Applications - 12th International Conference, ADMA 2016, Proceedings",
address = "Germany",
}