Results 41 to 50 of about 74,563 (305)
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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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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Multi-type relational clustering approaches : current state-of-the-art and new directions [PDF]
, 2008 The proliferation of multi-type relational datasets in a number of important real-world applications and the limitations resulting from the transformation of such datasets to fit propositional data mining approaches have led to the emergence of the ...
Anand, Sarabjot Singh, Li, Tao
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Spectral Embedded Deep Clustering
Entropy, 2019 We propose a new clustering method based on a deep neural network. Given an unlabeled dataset and the number of clusters, our method directly groups the dataset into the given number of clusters in the original space.
Yuichiro Wada +5 more
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Multiclass spectral clustering [PDF]
Proceedings Ninth IEEE International Conference on Computer Vision, 2003 We propose a principled account on multiclass spectral clustering. Given a discrete clustering formulation, we first solve a relaxed continuous optimization problem by eigen-decomposition. We clarify the role of eigenvectors as a generator of all optimal solutions through orthonormal transforms.
Stella X. Yu, Jianbo Shi
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Directional clustering through matrix factorization [PDF]
, 2016 This paper deals with a clustering problem where feature vectors are clustered depending on the angle between feature vectors, that is, feature vectors are grouped together if they point roughly in the same direction.
Blumensath, Thomas
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Active Spectral Clustering [PDF]
2010 IEEE International Conference on Data Mining, 2010 The technique of spectral clustering is widely used to segment a range of data from graphs to images. Our work marks a natural progression of spectral clustering from the original passive unsupervised formulation to our active semi-supervised formulation. We follow the widely used area of constrained clustering and allow supervision in the form of pair
Xiang Wang 0001, Ian Davidson
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Spectral Clustering of Mixed-Type Data
Stats, 2021 Cluster analysis seeks to assign objects with similar characteristics into groups called clusters so that objects within a group are similar to each other and dissimilar to objects in other groups.
Felix Mbuga, Cristina Tortora
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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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