Mutual kNN based spectral clustering

The key step of spectral clustering is learning the affinity matrix to measure the similarity among data points. This paper proposes a new spectral clustering method, which uses mutual k nearest neighbor to obtain the affinity matrix by removing the influence of noise. Then, the characteristics of h...

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Bibliographic Details
Published in:Neural computing & applications 2020-06, Vol.32 (11), p.6435-6442
Main Authors: Tan, Malong, Zhang, Shichao, Wu, Lin
Format: Article
Language:English
Subjects:
Affinity
Artificial Intelligence
Clustering
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Data points
Image Processing and Computer Vision
Multi-Source Data Understanding (MSDU)
Performance enhancement
Probability and Statistics in Computer Science
Spectra
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Summary:The key step of spectral clustering is learning the affinity matrix to measure the similarity among data points. This paper proposes a new spectral clustering method, which uses mutual k nearest neighbor to obtain the affinity matrix by removing the influence of noise. Then, the characteristics of high-dimensional data are self-represented to ensure local important information of data by using affinity matrix in standardized processing. Furthermore, we also use the normalization method to further improve the performance of clustering. Experimental analysis on eight benchmark data sets showed that our proposed method outperformed the state-of-the-art clustering methods in terms of clustering performance such as cluster accuracy and normalized mutual information.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-018-3836-z