A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative Clustering of High Dimensional Text Data Augmented by Local Search
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Minimum sum-squared residue for fuzzy co-clustering
Intelligent Data Analysis
Learning concepts from large scale imbalanced data sets using support cluster machines
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Relational clustering by symmetric convex coding
Proceedings of the 24th international conference on Machine learning
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding dense and isolated submarkets in a sponsored search spending graph
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A matrix-based multilevel approach to identify functional protein modules
International Journal of Bioinformatics Research and Applications
Graph summarization with bounded error
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
An Enzyme-Inspired Approach to Surmount Barriers in Graph Bisection
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
Selecting the Right Features for Bipartite-Based Text Clustering
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Applying Electromagnetic Field Theory Concepts to Clustering with Constraints
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Graph partitioning based on link distributions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Using graph partitioning to discover regions of correlated spatio-temporal change in evolving graphs
Intelligent Data Analysis
ADBIS'07 Proceedings of the 11th East European conference on Advances in databases and information systems
Term weighting evaluation in bipartite partitioning for text clustering
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Profiling users in a 3g network using hourglass co-clustering
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Identifying hotspots on the real-time web
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Transient crowd discovery on the real-time social web
Proceedings of the fourth ACM international conference on Web search and data mining
Fuse: towards multi-level functional summarization of protein interaction networks
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
A multilevel approach to identify functional modules in a yeast protein-protein interaction network
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
Cost-effective on-demand associative author name disambiguation
Information Processing and Management: an International Journal
Content-based crowd retrieval on the real-time web
Proceedings of the 21st ACM international conference on Information and knowledge management
Proceedings of the sixth ACM international conference on Web search and data mining
Communities and Balance in Signed Networks: A Spectral Approach
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Anatomy of a web-scale resale market: a data mining approach
Proceedings of the 22nd international conference on World Wide Web
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Graph clustering (also called graph partitioning) --- clustering the nodes of a graph --- is an important problem in diverse data mining applications. Traditional approaches involve optimization of graph clustering objectives such as normalized cut or ratio association; spectral methods are widely used for these objectives, but they require eigenvector computation which can be slow. Recently, graph clustering with a general cut objective has been shown to be mathematically equivalent to an appropriate weighted kernel k-means objective function. In this paper, we exploit this equivalence to develop a very fast multilevel algorithm for graph clustering. Multilevel approaches involve coarsening, initial partitioning and refinement phases, all of which may be specialized to different graph clustering objectives. Unlike existing multilevel clustering approaches, such as METIS, our algorithm does not constrain the cluster sizes to be nearly equal. Our approach gives a theoretical guarantee that the refinement step decreases the graph cut objective under consideration. Experiments show that we achieve better final objective function values as compared to a state-of-the-art spectral clustering algorithm: on a series of benchmark test graphs with up to thirty thousand nodes and one million edges, our algorithm achieves lower normalized cut values in 67% of our experiments and higher ratio association values in 100% of our experiments. Furthermore, on large graphs, our algorithm is significantly faster than spectral methods. Finally, our algorithm requires far less memory than spectral methods; we cluster a 1.2 million node movie network into 5000 clusters, which due to memory requirements cannot be done directly with spectral methods.