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Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015
Ensemble clustering, also known as consensus clustering, is emerging as a promising solution for multi-source and/or heterogeneous data clustering. The co-association matrix based method, which redefines the ensemble clustering problem as a classical graph partition problem, is a landmark method in this area.
Hongfu Liu +4 more
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Ensemble clustering, also known as consensus clustering, is emerging as a promising solution for multi-source and/or heterogeneous data clustering. The co-association matrix based method, which redefines the ensemble clustering problem as a classical graph partition problem, is a landmark method in this area.
Hongfu Liu +4 more
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Compressed Spectral Clustering
2009 IEEE International Conference on Data Mining Workshops, 2009Compressed sensing has received much attention in both data mining and signal processing communities. In this paper, we provide theoretical results to show that compressed spectral clustering, separating data samples into different clusters directly in the compressed measurement domain, is possible.
Bin Zhao, Changshui Zhang
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Compressive Spectral Clustering
AIP Conference Proceedings, 2010Data mining has become one of the fastest growing research topics in mathematics and computer science. Data such as high dimensional signals, magnetic resonance images, and hyperspectral images can be costly to acquire or it could be unobtainable to make even simple direct comparisons. Compressed sensing is a technique that addresses this issue.
Blake Hunter +4 more
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Incremental Spectral Clustering
2012In the present contribution, a novel algorithm for off-line spectral clustering algorithm is introduced and an online extension is derived in order to deal with sequential data. The proposed algorithm aims at dealing with nonconvex clusters having different forms.
Abdelhamid Bouchachia, Markus Prossegger
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Approximate Spectral Clustering
2009While spectral clustering has recently shown great promise, computational cost makes it infeasible for use with large data sets. To address this computational challenge, this paper considers the problem of approximate spectral clustering, which enables both the feasibility (of approximately clustering in very large and unloadable data sets) and ...
Liang Wang +3 more
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2008
In this paper, we propose a novel spectral clustering algorithm called: Locality Spectral Clustering ( Lsc ) which assumes that each data point can be linearly reconstructed from its local neighborhoods. The Lsc algorithm firstly try to learn a smooth enough manifold structure on the data manifold and then computes the eigenvectors on the smooth ...
Yun-Chao Gong, Chuanliang Chen
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In this paper, we propose a novel spectral clustering algorithm called: Locality Spectral Clustering ( Lsc ) which assumes that each data point can be linearly reconstructed from its local neighborhoods. The Lsc algorithm firstly try to learn a smooth enough manifold structure on the data manifold and then computes the eigenvectors on the smooth ...
Yun-Chao Gong, Chuanliang Chen
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Unified One-Step Multi-View Spectral Clustering
IEEE Transactions on Knowledge and Data Engineering, 2023Chang Tang, Zhenglai Li, Jun Wang
exaly
Spectral clustering is a well-known method for grouping objects by analyzing the spectral properties of a similarity matrix. It is based on graph theory and is particularly suitable for nonlinearly separable cluster structures. Typically, clusters are obtained through a hard partitioning of the data.
Di Nuzzo, Cinzia, Zaccaria, Giorgia
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Di Nuzzo, Cinzia, Zaccaria, Giorgia
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