Non-negative and sparse spectral clustering
Pattern Recognition
| Hi-index | 0.03 |
A two-way partitioning algorithm is presented that significantly improves on the highly unstable results typically obtained from the traditional Kernighan-Lin-based algorithms. The algorithm groups highly connected components into clusters and rearranges the clusters into two final subsets with specified sizes. It is known that the grouping operations reduce the complexity, and thus improve the results, of partitioning very large circuits. However, if the grouping is inappropriate, the partitioning results may degenerate. To prevent degeneration, a ratio cut approach is used to do the grouping. By a series of experiments based on the tradeoff between cut weight and CPU time, the value which controls the resultant number of groups is determined. Good experimental results have been observed for cut weight and CPU time