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Ultra-Scalable Spectral Clustering and Ensemble Clustering [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2019 This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble ...
Huang, Dong +4 more
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An improved multi-view spectral clustering based on tissue-like P systems [PDF]
Scientific Reports, 2022 Multi-view spectral clustering is one of the multi-view clustering methods widely studied by numerous scholars. The first step of multi-view spectral clustering is to construct the similarity matrix of each view.
Huijian Chen, Xiyu Liu
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Spectral Clustering Community Detection Algorithm Based on Point-Wise Mutual Information Graph Kernel [PDF]
Entropy, 2023 To address the problem that traditional spectral clustering algorithms cannot obtain the complete structural information of networks, this paper proposes a spectral clustering community detection algorithm, PMIK-SC, based on the point-wise mutual ...
Yinan Chen, Wenbin Ye, Dong Li
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Fault Diagnosis by Multisensor Data: A Data-Driven Approach Based on Spectral Clustering and Pairwise Constraints [PDF]
Sensors, 2020 This paper deals with clustering based on feature selection of multisensor data in high-dimensional space. Spectral clustering algorithms are efficient tools in signal processing for grouping datasets sampled by multisensor systems for fault diagnosis ...
Massimo Pacella, Gabriele Papadia
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Scientific Reports, 2017 A major challenge in clinical cancer research is the identification of accurate molecular subtype. While unsupervised clustering methods have been applied for class discovery, this clustering method remains a bottleneck in developing accurate method for ...
Mingguang Shi, Guofu Xu
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Large Scale Spectral Clustering Using Approximate Commute Time Embedding [PDF]
, 2012 Spectral clustering is a novel clustering method which can detect complex shapes of data clusters. However, it requires the eigen decomposition of the graph Laplacian matrix, which is proportion to $O(n^3)$ and thus is not suitable for large scale ...
C. Fowlkes +10 more
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Fair Method for Spectral Clustering to Improve Intra-cluster Fairness [PDF]
Jisuanji kexue, 2023 Recently,the fairness of the algorithm has aroused extensive discussion in the machine learning community.Given the widespread popularity of spectral clustering in modern data science,studying the algorithm fairness of spectral clustering is a crucial ...
XU Xia, ZHANG Hui, YANG Chunming, LI Bo, ZHAO Xujian
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Spectral Clustering by Subspace Randomization and Graph Fusion for High-Dimensional Data [PDF]
Advances in Knowledge Discovery and Data Mining24th Pacific-Asia Conference, 2020 Cai X, Huang D, Wang C, Kwoh C.
europepmc +3 more sources
A new Kmeans clustering model and its generalization achieved by joint spectral embedding and rotation [PDF]
PeerJ Computer Science, 2021 The Kmeans clustering and spectral clustering are two popular clustering methods for grouping similar data points together according to their similarities.
Wenna Huang +3 more
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مجلة بغداد للعلوم, 2021 This study focused on spectral clustering (SC) and three-constraint affinity matrix spectral clustering (3CAM-SC) to determine the number of clusters and the membership of the clusters of the COST 2100 channel model (C2CM) multipath dataset ...
Jojo Blanza
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