基于密度与路径的稳健谱聚类.pdfVIP

Computer Engineering and Applications 计算机工程与应用 2015 ，51（2） 165 基于密度与路径的稳健谱聚类 许洪玮，曹江中，何家峰，戴青云 XU Hongwei, CAO Jiangzhong, HE Jiafeng, DAI Qingyun 广东工业大学 信息工程学院，广州 510006 School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China XU Hongwei, CAO Jiangzhong, HE Jiafeng, et al. Robust density-path-based spectral clustering. Computer Engi- neering and Applications, 2015, 51（2）：165-170. Abstract ：Spectral clustering is wildly studied in the field of class identification in recent years, of which the path-based and density-based algorithms are the two main research topics. These two algorithms have delivered impressive results in some data sets, but are ineffective for some special cases. This paper unites the advantages of these two algorithms and finds the paths under the multi-levels restriction of density, after which a new similarity measure is built. In order to enhance the robustness against noises, robust coefficients are added based on the local information of data set, thus a robust density- path-based spectral clustering method is proposed. Experimental results on synthetic data sets as well as real world data sets demonstrate that the proposed method can obtain more than acceptable results. Key words ：spectral clustering; path-based spectral clustering; density-based spectral clustering 摘 要：近年来，谱聚类在分类领域得到了广泛的研究，其中基于路径和基于密度的算法是两个重要的研究方向。 虽然这两种算法在一些数据集上能取得较好的分类效果，但不能对一些特殊的数据集进行准确分类。融合了这两 种方法的优点，通过多级密度约束来寻找路径，根据得到的路径建立新的相似性矩阵。为了加强对噪声的鲁棒性， 根据数据集的局部信息加入鲁棒性系数，提出了基于路径与密度的稳健谱聚类算法。实验结果表明该方法在人工 数据集和手写体数据集上能取得较理想的分类结果。 关键词：谱聚类；基于路径的谱聚类；基于密度的谱聚类 文献标志码：A 中图分类号：TP391 doi ：10.3778/j.issn.1002-8331.1303-0096 1 引言 谱聚类算法使用高斯核函数来计算相似性矩阵，经 聚类作为重要的分析方法，是模式识别、机器学习、 过对相似性矩阵的中间处理后，最后使用k-means 算法 数据挖掘等现代数据处理领域的重要研究课题。经过 或其他算法聚类。该算法实现简单且计算速度快，它虽 十几年的发展，学者们已经提出了许多效果不错的聚类 然在一些