Spectral clustering with mixed Euclidean and Kendall Tau metrics
GUANG Jun-ye
SHAO Wei
SUN Liang
ZHANG Dao-qiang
Abstract：Spectral methods have been largely utilized in clustering problems. Most of existing methods ignore the useful information from neighborhoods and only employ conventional metric to evaluate the similarity between pairs of samples. Accordingly, this paper proposes a novel spectral clustering method with mixed Euclidean and Kendall Tau met-rics (SCMEK), by which similarities between pairs of samples and their neighbors are both considered for learning the underlying structure of the datasets. Specifically, the new similarity metric is a fusion algorithm, which outputs enhanced metric by combining multiple metrics (i.e., Euclidean metric and Kendall Tau metric). Moreover, the proposed method uti-lizes the non-linear fusion of different similarity metrics to tackle the dataset from different aspects and thus can effectively utilize different information from the data structure. Experimental study on various datasets demonstrates that the proposed approach achieves superior performance to state-of-the-art methods.
Keywords：Kendall Tau distancedistance metricsimilarity fusionspectral clustering
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 783-789 )