Results 31 to 40 of about 234,781 (273)
Complexity, 2021 Abnormal behavior detection of social security funds is a method to analyze large-scale data and find abnormal behavior. Although many methods based on spectral clustering have achieved many good results in the practical application of clustering, the ...
Yan Wu, Yonghong Chen, Wenhao Ling
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Identifying cell types from single-cell data based on similarities and dissimilarities between cells
BMC Bioinformatics, 2021 Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research.
Yuanyuan Li +3 more
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Fast kernel spectral clustering [PDF]
Neurocomputing, 2017 Abstract Spectral clustering suffers from a scalability problem in both memory usage and computational time when the number of data instances N is large. To solve this issue, we present a fast spectral clustering algorithm able to effectively handle millions of datapoints at a desktop PC scale.
Langone, Rocco, Suykens, Johan
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, 2013 With inspiration from Random Forests (RF) in the context of classification, a new clustering ensemble method---Cluster Forests (CF) is proposed. Geometrically, CF randomly probes a high-dimensional data cloud to obtain "good local clusterings" and then ...
Chen, Aiyou +2 more
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KNN-SC: Novel Spectral Clustering Algorithm Using k-Nearest Neighbors
IEEE Access, 2021 Spectral clustering is a well-known graph-theoretic clustering algorithm. Although spectral clustering has several desirable advantages (such as the capability of discovering non-convex clusters and applicability to any data type), it often leads to ...
Jeong-Hun Kim +4 more
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Spectral redemption: clustering sparse networks [PDF]
, 2013 Spectral algorithms are classic approaches to clustering and community detection in networks. However, for sparse networks the standard versions of these algorithms are suboptimal, in some cases completely failing to detect communities even when other ...
Krzakala, Florent +6 more
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IEEE Access, 2023 This study embarks on an in-depth analysis of the performance of various kernel functions, namely uniform, epanechnikov, triangular, and gaussian, in window-based and spectral clustering-based models.
Petr Silhavy, Radek Silhavy
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Spectral clustering with epidemic diffusion [PDF]
Physical Review E, 2013 6 pages, to appear in Physical Review ...
Smith, Laura M. +4 more
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Affinity Matrix Learning Via Nonnegative Matrix Factorization for Hyperspectral Imagery Clustering
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 In this article, we integrate the spatial-spectral information of hyperspectral image (HSI) samples into nonnegative matrix factorization (NMF) for affinity matrix learning to address the issue of HSI clustering.
Yao Qin +5 more
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, 2023 AbstractIn this paper, we present a spectral clustering approach for clustering three-way data. Three-way data concern data characterized by three modes: n units, p variables, and t different occasions. In other words, three-way data contain a t × p observed matrix for each statistical observation.
Di Nuzzo Cinzia, Ingrassia Salvatore
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