Spectral Clustering Approach with K-Nearest Neighbor and Weighted Mahalanobis Distance for Data Mining

Spectral Clustering Approach with K-Nearest Neighbor and Weighted Mahalanobis Distance for Data Mining

This paper proposes a spectral clustering method using k-means and weighted Mahalanobis distance (Referred to as MDLSC) to enhance the degree of correlation between data points and improve the clustering accuracy of Laplacian matrix eigenvectors. First, we used the correlation coefficient as the wei...

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Bibliographic Details
Main Authors: Lifeng Yin, Lei Lv, Dingyi Wang, Yingwei Qu, Huayue Chen, Wu Deng
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:Electronics
Subjects:
data mining
spectral clustering
Mahalanobis distance
Laplace matrix
K-means clustering
Online Access:https://www.mdpi.com/2079-9292/12/15/3284

Internet

https://www.mdpi.com/2079-9292/12/15/3284

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