Abstract
Density peaks (DP) clustering is a new density-based clustering method. This algorithm can deal with some data sets having non-convex clusters. However, when the shape of clusters is very complicated, it cannot find the optimal structure of clusters. In other words, it cannot discover arbitrary shaped clusters. In order to solve this problem, a new hybrid clustering method, called L-DP, is proposed in this paper combines density peaks clustering with the leader clustering method. Experiments on synthetic datasets show L-DP could be a suitable one for arbitrary shaped clusters compared with the original DP clustering method. The experimental results on real-world data sets demonstrate that the proposed algorithm is competitive with the state-of-the-art clustering algorithms, such as DP, AP and DBSCAN.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)
Li, X., Liang, Y., Cai, Y.: CC-K-means: a candidate centres-based k-means algorithm for text data. Int. J. Collaborative Intell. 1(3), 189–204 (2016)
Kou, F., Du, J., He, Y.: Social network search based on semantic analysis and learning. CAAI Trans. Intell. Technol. 1(2), 173–178 (2016)
Xu, R., Wunsch, D.: Survey of clustering algorithms. In: IEEE Trans. Neural Networks 16(3), 645–678 (2005)
Ma, Z., Liu, Q., Sun, K.: A syncretic representation for image classification and face recognition. In: CAAI Trans. Intell. Technol. 1(2) pp. 173–178 (2016)
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)
Ester, M., Kriegel, H.P., Sander, J., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of Second International Conference on Knowledge Discovery and Data Mining, vol. 96(34), pp. 226–231 (1996)
Chen, Y. W., Lai, D.H., Qi, H., et al.: A new method to estimate ages of facial image for large database. Multimedia Tools Appl. 75(5), 2877–2895 (2016)
Wang, B., Zhang, J., Liu, Y., et al.: Density peaks clustering based integrate framework for multi-document summarization. CAAI Trans. Intell. Technol. (2017)
Bai, X., Yang, P., Shi, X.: An overlapping community detection algorithm based on DensityPeaks. Neurocomputing 226, 7–15 (2016)
Mehmood, R., Zhang, G., Bie, R., et al.: Clustering by fast search and find of density peaks via heat diffusion. Neurocomputing 208, 210–217 (2016)
Du, M., Ding, S., Jia, H.: Study on density peaks clustering based on k-nearest neighbors and principal component analysis. Knowl. Based Syst. 99, 135–145 (2016)
Spath, H.: Cluster Analysis Algorithms for Data Reduction and Classification. Ellis Horwood, Chichester (1980)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (Nos.61672522,61379101), and the China Postdoctoral Science Foundation (No. 2016M601910).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Du, M., Ding, S. (2017). L-DP: A Hybrid Density Peaks Clustering Method. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-61845-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61844-9
Online ISBN: 978-3-319-61845-6
eBook Packages: Computer ScienceComputer Science (R0)