Slaninova. Spectral clustering: Left-right-oscillate al- gorithm for detecting communities. In M. Pechenizkiy and M. Wojciechowski, editors, New Trends in Databases and Information Systems, volume 185 of Advances in Intelligent Sys- tems and Computing, pages 285-294. Springer Berlin Heidelberg,...
We proposed spectral clustering with robust self-learning constraints (RSLC) in this paper. In the new algorithm, we extend the objective function of the classical semi-supervised spectral clustering model by seeing label constraints as variables and adding a robust function. We wish to minimize the...
In this paper, we provide a new insight into clustering with a spring–mass dynamics, and propose a resulting hierarchical clustering algorithm. To realize the spectral graph partitioning as clustering, we model a weighted graph of a data set as a mass–spring dynamical system, where we regard...
A major challenge in clinical cancer research is the identification of accurate molecular subtype. While unsupervised clustering methods have been applied for class discovery, this clustering method remains a bottleneck in developing accurate method for molecular subtype discovery. In this analysis, we hyp...
UTC: Uniform Tensor Clustering by Jointly Exploring Sample Affinities of Various Orders, 2024 TNNLS. CRMATS: Multiview Tensor Spectral Clustering via Co-Regularization, 2024 TPAMI. 主要研究的是tensor spectral clustering以及Multiview tensor spectral clustering。编辑...
a measure oftemporal smoothnessis integrated in the overall measure of clustering quality. In this paper, we propose two frameworks that incorporate temporal smoothness in evolutionary spectral clustering. For both frameworks, we start with intuitions gained from the well-knownk-means clustering...
clustering or partitioning, in essence an appeal to theprinciple of divide-and-conquer. However, while the output of a clustering algorithm may yield aset of smaller-scale problems that may be easier to tackle, clustering algorithms can themselves becomplex, and large-scale clustering often ...
Westin, "Segmentation of thalamic nuclei from DTI using spectral clustering", Proc. Med. Image Comput. Comp. Assisted Intervention , pp. 807-814, 2006U. Ziyan, D. Tuch, and C.-F. Westin, "Segmentation of tha- lamic nuclei from DTI using spectral clustering," in Medical Image Computing ...
American Journal of Operations ResearchY.-P. Fang and E. Zio, "Hierarchical modeling by recursive unsuper- vised spectral clustering and network extended importance measures to analyze the reliability characteristics of complex network systems," Amer. J. Oper. Res., vol. 3, no. 1A, pp. 101-...
The theoretical analysis of spectral clustering mainly focuses on consistency, while there is relatively little research on its generalization performance. In this paper, we study the excess risk bounds of the popular spectral clustering algorithms: \emph{relaxed} RatioCut and \emph{relaxed} NCut. ...