Results 11 to 20 of about 74,563 (305)
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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Power Spectral Clustering [PDF]
Journal of Mathematical Imaging and Vision, 2020 International audienceThe problem of clustering has been an important problem since the early 20th century and several possible solutions were proposed. With the rise of computing machines clustering has become an important part of many data mining tasks,
Challa, Aditya, +8 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2020 In the past decades, spectral clustering (SC) has become one of the most effective clustering algorithms. However, most previous studies focus on spectral clustering tasks with a fixed task set, which cannot incorporate with a new spectral clustering ...
Li, Jun +4 more
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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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A tutorial on spectral clustering [PDF]
Statistics and Computing, 2006 In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms ...
von Luxburg, U.
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A Note On Spectral Clustering [PDF]
CoRR, 2015 Spectral clustering is a popular and successful approach for partitioning the nodes of a graph into clusters for which the ratio of outside connections compared to the volume (sum of degrees) is small.
Mehlhorn, Kurt +5 more
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Correlational spectral clustering
2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008 We present a new method for spectral clustering with paired data based on kernel canonical correlation analysis, called correlational spectral clustering. Paired data are common in real world data sources, such as images with text captions.
Blaschko, M. +3 more
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Semidefinite spectral clustering
Pattern Recognition, 2019 Multi-way partitioning of an undirected weighted graph where pairwise similarities are assigned as edge weights, provides an important tool for data clustering, but is an NP-hard problem.
Kim, J, Choi, S
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Consistency of Spectral Clustering
The Annals of Statistics, 2007 Consistency is a key property of statistical algorithms when the data is drawn from some underlying probability distribution. Surprisingly, despite decades of work, little is known about consistency of most clustering algorithms.
Bousquet, O. +5 more
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Discrete nonnegative spectral clustering
IEEE Transactions on Knowledge and Data Engineering, 2017 Spectral clustering has been playing a vital role in various research areas. Most traditional spectral clustering algorithms comprise two independent stages (e.g., first learning continuous labels and then rounding the learned labels into discrete ones),
Heng Tao Shen +14 more
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