Results 41 to 50 of about 234,781 (273)
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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Partitioning Well-Clustered Graphs: Spectral Clustering Works! [PDF]
SIAM Journal on Computing, 2017 In this paper we study variants of the widely used spectral clustering that partitions a graph into k clusters by (1) embedding the vertices of a graph into a low-dimensional space using the bottom eigenvectors of the Laplacian matrix, and (2) grouping the embedded points into k clusters via k-means algorithms. We show that, for a wide class of graphs,
Zanetti, Luca, Sun, He, Peng, Richard
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Large Scale Spectral Clustering Using Approximate Commute Time Embedding
, 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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Subspace clustering via thresholding and spectral clustering [PDF]
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013 ICASSP ...
Heckel, Reinhard, Bölcskei, Helmut
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The geometry of kernelized spectral clustering [PDF]
, 2014 Clustering of data sets is a standard problem in many areas of science and engineering. The method of spectral clustering is based on embedding the data set using a kernel function, and using the top eigenvectors of the normalized Laplacian to recover ...
Schiebinger, Geoffrey +2 more
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An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors
Algorithms, 2015 The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix.
Xiaoqi He, Sheng Zhang, Yangguang Liu
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Consistency of spectral clustering
The Annals of Statistics, 2008 Published in at http://dx.doi.org/10.1214/009053607000000640 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
von Luxburg, Ulrike +2 more
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Compressive Spectral Clustering [PDF]
, 2016 Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a ...
Gribonval, Remi +3 more
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Organoids in pediatric cancer research
FEBS Letters, EarlyView.Organoid technology has revolutionized cancer research, yet its application in pediatric oncology remains limited. Recent advances have enabled the development of pediatric tumor organoids, offering new insights into disease biology, treatment response, and interactions with the tumor microenvironment.
Carla Ríos Arceo, Jarno Drost
wiley +1 more source
Frontiers in Energy Research, 2022 Power system clustering is an effective method for realizing voltage control and preventing failure propagation. Various approaches are used for power system clustering. Graph-theory-based spectral clustering methods are widely used because they follow a
Juhwan Kim +5 more
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