快速特征映射优化的流形密度峰聚类

南京大学学报(自然科学版) ›› 2018, Vol. 54 ›› Issue (4): 838–.

快速特征映射优化的流形密度峰聚类

朱庆峰1，2，葛洪伟1，2*

出版日期:2018-04-30
作者简介:1.轻工过程先进控制教育部重点实验室，江南大学，无锡，214122；2.江南大学物联网工程学院，无锡，214122
基金资助:
基金项目：江苏省高校研究生科研创新计划(KYLX15_1169) 收稿日期：2018－03－29 *通讯联系人，E－mail：clanyzhu@163.com com

Manifold density peak clustering optimized by fast feature mapping

Zhu Qingfeng1，2，Ge Hongwei1，2*

Online:2018-04-30
About author:1.Ministry of Education Key Laboratory of Advanced Process Control for Light Industry，Jiangnan University，Wuxi，214122，China； 2.School of Internet of Things，Jiangnan University，Wuxi，214122，China

摘要/Abstract

摘要： 经典的密度峰聚类不再适用于复杂的流形聚类，因此提出了快速特征映射优化的流形密度峰聚类，用快速特征映射优化的流形距离取代欧式距离，可以更好地反映不同类的点间相似性. 算法首先通过寻找特征点，构造无向特征图，再通过无向特征图计算任意两个点之间的流形距离，最后按照流形距离的大小完成分配. 在人工数据集和UCI数据集上的实验表明，新算法具有更高的准确率．

Abstract: For complex manifold clustering，density peak clustering is no longer applicable，and fast feature mapping is proposed to optimize the manifold density clustering in this paper. By using the fast feature map to optimize the manifold distance to replace the Euclidean distance，it is better to reflect the similarity between different classes of points. The algorithm first constructs the undirected feature graph by looking for the feature points. For any two points，the manifold distance between them is calculated by the undirected feature graph，and finally the distribution is done according to the size of the manifold. Experiments on artificial datasets and UCI datasets show that the new algorithm has a higher accuracy rate.

朱庆峰1，2，葛洪伟1，2*. 快速特征映射优化的流形密度峰聚类[J]. 南京大学学报(自然科学版), 2018, 54(4): 838–.

Zhu Qingfeng1，2，Ge Hongwei1，2*. Manifold density peak clustering optimized by fast feature mapping[J]. Journal of Nanjing University(Natural Sciences), 2018, 54(4): 838–.

参考文献

[1]　Jain A K. Data clustering：50 years beyond K－means. Pattern Recognition Letters，2010，31(8)：651－666. [2] Park H S，Jun C H. A simple and fast algorithm for K－medoids clustering. Expert Systems with Applications，2009，36(2)：3336－3341. [3] 周亚建，徐　晨，李继国. 基于改进CURE聚类算法的无监督异常检测方法. 通信学报，2010，31(7)：18－23，32.(Zhou Y J，Xu C，Li J G. Unsupervised anomaly detection method based on improved CURE clustering algorithm. Journal on Communications，2010，31(7)：18－23，32.) [4] Ansari S，Chetlur S，Prabhu S，et al. An overview of clustering analysis techniques used in data miniing. International Journal of Emerging Technology and Advanced Engineering，2013，3(12)：284－286. [5] Amini A，Wah T Y，Saybani M R，et al. A study of density－grid based clustering algorithms on data streams ∥ Proceedings of 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery. Shanghai，China：IEEE，2011，3：1652－1656. [6] Yang P，Zhu Q S，Huang B. Spectral clustering with density sensitive similarity function. Knowledge－Based Systems，2011，24(5)：621－628. [7] 杨　静，高嘉伟，梁吉业等. 基于数据场的改进DBSCAN聚类算法. 计算机科学与探索，2012，6(10)：903－911.(Yang J，Gao J W，Liang J Y，et al. An improved DBSCAN clustering algorithm based on data field. Journal of Frontiers of Computer Science and Technology，2012，6(10)：903－911.) [8] Kalita H K，Bhattacharya D K，Kar A. A new algorithm for ordering of points to identify clustering structure based on perimeter of triangle：Optics(BOPT) ∥ Proceedings of 2007 International Conference on Advanced Computing and Communications. Guwahati，India：IEEE，2007：523－528. [9] Rodriguez A，Laio A. Clustering by fast search and find of density peaks. Science，2014，344(6191)：1492－1496. [10] 马春来，单　洪，马　涛. 一种基于簇中心点自动选择策略的密度峰值聚类算法. 计算机科学，2016，43(7)：255－258，280.(Ma C L，Shan H，Ma T. Improved density peaks based clustering algorithm with strategy choosing cluster center automatically. Computer Science，2016，43(7)：255－258，280.) [11] Bie R F，Mehmood R，Ruan S S，et al. Adaptive fuzzy clustering by fast search and find of density peaks. Personal and Ubiquitous Computing，2016，20(5)：785－793. [12] 李　涛，葛洪伟，苏树智. 基于密度自适应距离的密度峰聚类. 小型微型计算机系统，2017，38(6)：1347－1352.( Li T，Ge H W，Su S Z. Density peaks clustering based on density adaptive distance. Journal of Chinese Computer Systems，2017，38(6)：1347－1352.) [13] 谢娟英，高红超，谢维信. K近邻优化的密度峰值快速搜索聚类算法. 中国科学：信息科学，2016，46(2)：258－280.(Xie J Y，Gao H C，Xie W X. K－nearest neighbors optimized clustering algorithm by fast search and finding the density peaks of a dataset. Scientia Sinica：Informationis，2016，46(2)：258－280.) [14] 杨　燕，靳　蕃，Mohamed K. 聚类有效性评价综述. 计算机应用研究，2008，25(6)：1630－1632，1638.(Yang Y，Jin F，Mohamed K. Survey of clustering validity evaluation. Application Research of Computers，2008，25(6)：1630－1632，1638.) [15] Vinh N X，Epps J，Bailey J. Information theoretic measures for clusterings comparison：Is a correction for chance necessary? ∥ Proceedings of the 26th Annual International Conference on Machine Learning. Montreal，Canada：ACM，2009：1073－1080. [16] Rehm F，Klawonn F，Kruse R. A novel approach to noise clustering for outlier detection. Soft Computing，2007，11(5)：489－494. [17] Ding S F，Zhang J，Jia H J，et al. An adaptive density data stream clustering algorithm. Cognitive Computation，2016，8(1)：30－38.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed