Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A matrix-based multilevel approach to identify functional protein modules
International Journal of Bioinformatics Research and Applications
Triangular clique based multilevel approaches to identify protein functional modules
VECPAR'06 Proceedings of the 7th international conference on High performance computing for computational science
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
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The divisive MinMaxCut algorithm of Ding et al.[3] produces more accurate clustering results than existing document cluster methods. Multilevel algorithms [4, 1, 5, 7] have been used to boost the speed of graph partitioning algorithms. We combine these two algorithms to construct faster and more accurate algorithm. In this new algorithm, the original graph is coarsened, partitioned by the divisive MinMaxCut algorithm and then decoarsened. A refining algorithm is also applied to improve the accuracy at each level.