Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Design and implementation of move-based heuristics for VLSI hypergraph partitioning
Journal of Experimental Algorithmics (JEA)
Design and Implementation of the Fiduccia-Mattheyses Heuristic for VLSI Netlist Partitioning
ALENEX '99 Selected papers from the International Workshop on Algorithm Engineering and Experimentation
Face recognition using spectral features
Pattern Recognition
Mean shift spectral clustering
Pattern Recognition
Towards effective document clustering: A constrained K-means based approach
Information Processing and Management: an International Journal
A spectral approach to clustering numerical vectors as nodes in a network
Pattern Recognition
Learning low-rank kernel matrices for constrained clustering
Neurocomputing
Intelligent photo clustering with user interaction and distance metric learning
Pattern Recognition Letters
Combining Relations and Text in Scientific Network Clustering
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Non-negative and sparse spectral clustering
Pattern Recognition
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Recent research on partitioning has focused on the ratio-cut cost metric, which maintains a balance between the cost of the edges cut and the sizes of the partitions without fixing the size of the partitions a priori. Iterative approaches and spectral approaches to two-way ratio-cut partitioning have yielded higher quality partitioning results. In this paper, we develop a spectral approach to multi-way ratio-cut partitioning that provides a generalization of the ratio-cut cost metric to L-way partitioning and a lower bound on this cost metric. Our approach involves finding the k smallest eigenvalue/eigenvector pairs of the Laplacian of the graph. The eigenvectors provide an embedding of the graph's n vertices into a k-dimensional subspace. We devise a time and space efficient clustering heuristic to coerce the points in the embedding into k partitions. Advancement over the current work is evidenced by the results of experiments on the standard benchmarks