A Multi-level Hypergraph Partitioning Algorithm Using Rough Set Clustering

The hypergraph partitioning problem has many applications in scientific computing and provides a more accurate inter-processor communication model for distributed systems than the equivalent graph problem. In this paper, we propose a sequential multi-level hypergraph partitioning algorithm. The algorithm makes novel use of the technique of rough set clustering in categorising the vertices of the hypergraph. The algorithm treats hyperedges as features of the hypergraph and tries to discard unimportant hyperedges to make better clustering decisions. It also focuses on the trade-off to be made between local vertex matching decisions (which have low cost in terms of the space required and time taken) and global decisions (which can be of better quality but have greater costs). The algorithm is evaluated and compared to state-ofthe-art algorithms on a range of benchmarks. The results show that it generates better partition quality.