Weighted Graph Cuts without Eigenvectors A Multilevel Approach

A variety of clustering algorithms have recently been proposed to handle data that is not linearly separable; spectral clustering and kernel k-means are two of the main methods. In this paper, we discuss an equivalence between the objective functions used in these seemingly different methods - in pa...

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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 29; no. 11; pp. 1944 - 1957
Main Authors:	Dhillon, I.S., Yuqiang Guan, Kulis, B.
Format:	Journal Article
Language:	English
Published:	Los Alamitos, CA IEEE 01.11.2007 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithm design and analysis Algorithms Applied sciences Artificial Intelligence Cluster Analysis Clustering Clustering algorithms Computer science; control theory; systems Data mining Data processing. List processing. Character string processing Eigenvectors Exact sciences and technology Graph Partitioning Graphs Image analysis Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image segmentation k-means Kernel Kernels Large-scale systems Mathematical analysis Memory organisation. Data processing Multilevel Network analysis Optimization methods Partitioning algorithms Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Reproducibility of Results Segmentation Sensitivity and Specificity Social network services Software Spectral Clustering Studies Cluster analysis Image processing K means algorithm Information extraction Data mining Graph decomposition Network analysis Classification Genetics segmentation Fast algorithm graph partitioning Pattern analysis Bioinformatics Multilevel system spectral clustering Data analysis Social network Graph theory Clustering Weighted graph Image segmentation Image analysis Graph cut Social analysis Objective function Graph method Artificial intelligence kernel k-means
ISSN:	0162-8828, 1939-3539
Online Access:	Get full text
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