基于融合欧氏距离与Kendall Tau距离度量的谱聚类算法（英文）
发布时间：2018-12-12 21:42
【摘要】：大多数现存的谱聚类方法均使用传统距离度量计算样本之间的相似性,这样仅仅考虑了两两样本之间的相似性而忽略了周围的近邻信息,更没有顾及数据的全局性分布结构.因此,本文提出一种新的融合欧氏距离和Kendall Tau距离的谱聚类方法.该方法通过融合两两样本之间的直接距离以及其周围的近邻信息,充分利用了不同的相似性度量可以从不同角度抓取数据之间结构信息的优势,更加全面地反映数据的底层结构信息.通过与传统聚类算法在UCI标准数据集上的实验结果作比较,验证了本文的方法可以显著提高聚类效果.
[Abstract]:Most of the existing spectral clustering methods use the traditional distance measure to calculate the similarity between samples, which only considers the similarity between pairwise samples and neglects the neighboring information, and does not take into account the global distribution structure of the data. Therefore, a new spectral clustering method combining Euclidean distance and Kendall Tau distance is proposed. By combining the direct distance between two samples and the adjacent information around it, the method makes full use of the advantages of different similarity measures to capture the structural information between data from different angles. A more comprehensive reflection of the underlying structure of the data information. By comparing with the experimental results of traditional clustering algorithm on UCI standard data set, it is verified that the proposed method can significantly improve the clustering effect.
【作者单位】： 南京航空航天大学计算机科学与技术学院;
【基金】：Supported by National Natural Science Foundation of China(61422204,61473149) Jiangsu Natural Science Foundation for Young Scholar(BK2013-0034) Foundation of Graduate Innovation Center in NUAA(KFJJ20151605)
【分类号】：TP311.13
[Abstract]:Most of the existing spectral clustering methods use the traditional distance measure to calculate the similarity between samples, which only considers the similarity between pairwise samples and neglects the neighboring information, and does not take into account the global distribution structure of the data. Therefore, a new spectral clustering method combining Euclidean distance and Kendall Tau distance is proposed. By combining the direct distance between two samples and the adjacent information around it, the method makes full use of the advantages of different similarity measures to capture the structural information between data from different angles. A more comprehensive reflection of the underlying structure of the data information. By comparing with the experimental results of traditional clustering algorithm on UCI standard data set, it is verified that the proposed method can significantly improve the clustering effect.
【作者单位】： 南京航空航天大学计算机科学与技术学院;
【基金】：Supported by National Natural Science Foundation of China(61422204,61473149) Jiangsu Natural Science Foundation for Young Scholar(BK2013-0034) Foundation of Graduate Innovation Center in NUAA(KFJJ20151605)
【分类号】：TP311.13
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