A new Kmeans clustering model and its generalization achieved by joint spectral embedding and rotation

The Kmeans clustering and spectral clustering are two popular clustering methods for grouping similar data points together according to their similarities. However, the performance of Kmeans clustering might be quite unstable due to the random initialization of the cluster centroids. Generally, spec...

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Bibliographic Details
Published in:PeerJ. Computer science 2021-03, Vol.7, p.e450-e450, Article e450
Main Authors: Huang, Wenna, Peng, Yong, Ge, Yuan, Kong, Wanzeng
Format: Article
Language:English
Subjects:
Algorithms
Algorithms and Analysis of Algorithms
Analysis
Artificial Intelligence
Centroids
Clustering
Communications industry
Data Mining and Machine Learning
Data points
Data similarity
Datasets
Discretization
Embedding
Kmeans clustering
Optimization
Performance evaluation
Rotation
Similarity
Spectra
Spectral clustering
Spectral rotation
Telecommunications services industry
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Summary:The Kmeans clustering and spectral clustering are two popular clustering methods for grouping similar data points together according to their similarities. However, the performance of Kmeans clustering might be quite unstable due to the random initialization of the cluster centroids. Generally, spectral clustering methods employ a two-step strategy of spectral embedding and discretization postprocessing to obtain the cluster assignment, which easily lead to far deviation from true discrete solution during the postprocessing process. In this paper, based on the connection between the Kmeans clustering and spectral clustering, we propose a new Kmeans formulation by joint spectral embedding and spectral rotation which is an effective postprocessing approach to perform the discretization, termed KMSR. Further, instead of directly using the dot-product data similarity measure, we make generalization on KMSR by incorporating more advanced data similarity measures and call this generalized model as KMSR-G. An efficient optimization method is derived to solve the KMSR (KMSR-G) model objective whose complexity and convergence are provided. We conduct experiments on extensive benchmark datasets to validate the performance of our proposed models and the experimental results demonstrate that our models perform better than the related methods in most cases.
ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.450