Density peaks clustering (DPC) algorithm is a novel clustering algorithm based on density. It needs neither iterative process nor more parameters. However, it cannot effectively group data with arbitrary shapes, or multi-manifold structures. To handle this drawback, we propose a new density peaks clustering, i.e., density peaks clustering using geodesic distances (DPC-GD), which introduces the idea of the geodesic distances into the original DPC method. By experiments on synthetic data sets, we reveal the power of the proposed algorithm. By experiments on image data sets, we compared our algorithm with classical methods (kernel k-means algorithm and spectral clustering algorithm) and the original algorithm in accuracy and NMI. Experimental results show that our algorithm is feasible and effective.
Data clustering Density peaks clustering Geodesic distances
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This work is supported by the National Natural Science Foundation of China (Nos. 61379101 and 61672522), the National Key Basic Research Program of China (No. 2013CB329502). The Priority Academic Program Development of Jiangsu Higer Education Institutions (PAPD), and the Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET).
Wen X, Shao L, Xue Y et al (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406CrossRefGoogle Scholar
Iam-On N, Boongoen T, Kongkotchawan N (2014) A new link-based method to ensemble clustering and cancer microarray data analysis. Int J Collab Intell 1(1):45–67Google Scholar
Jia H, Ding S, Du M et al (2016) Approximate normalized cuts without Eigen-decomposition. Inf Sci 374:135–150CrossRefGoogle Scholar
Zheng Y, Jeon B, Xu D et al (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28(2):961–973Google Scholar
Han J, Kamber M (2000) Data mining: concepts and techniques. Morgan Kaufman, San FranciscozbMATHGoogle Scholar
Zhang Y, Sun X, Wang B (2016) Efficient algorithm for k-barrier coverage based on integer linear programming. China Commun 13(7):16–23CrossRefGoogle Scholar
Li X, Liang Y, Cai Y (2016) CC-K-means: a candidate centres-based K-means algorithm for text data. Int J Collab Intell 1(3):189–204Google Scholar
Dong CR, Ng WWY, Wang XZ et al (2014) An improved differential evolution and its application to determining feature weights in similarity-based clustering. Neurocomputing 146:95–103CrossRefGoogle Scholar
Xu L, Ding S, Xu X et al (2016) Self-adaptive extreme learning machine optimized by rough set theory and affinity propagation clustering. Cognit Comput 8(4):720–728CrossRefGoogle Scholar
Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Sci 344(6191):1492–1496CrossRefGoogle Scholar
Chen GJ, Zhang XY, Wang ZJ et al (2015) Robust support vector data description for outlier detection with noise or uncertain data. Knowl-Based Syst 90:129–137CrossRefGoogle Scholar
Lu KY, Xia SY, Xia C (2015) Clustering based road detection method. In: Proceedings of the 34th Chinese Control Conference (CCC). pp 3874–3879Google Scholar
Xie K, Wu J, Yang W, Sun CY (2015) K-means clustering based on density for scene image classification. In: Proceedings of the 2015 Chinese Intelligent Automation Conference. pp 379–386Google Scholar
Zhang Y, Xia Y, Liu Y et al (2015) Clustering sentences with density peaks for multi-document summarization. In: Proceedings of human language technologies: the 2015 annual conference of the north american chapter of the ACL. pp 1262–1267Google Scholar
Tang GH, Jia S, Li J (2015) An enhanced density peak-based clustering approach for hyperspectral band selection. In: Proceedings of the international geoscience and remote sensing symposium. pp 1116–1119Google Scholar
Zhang WK, Li J (2015) Extended fast search clustering algorithm: widely density clusters, no density peaks. arXiv preprint arXiv:1505.05610. doi:10.5121/csit.2015.50701