G. Karypis, and V. Kumar. In Proceedings of the Design and Automation Conference, page 343--348. (1998)
Abstract
In this paper, we present a new multilevel k-way hypergraph partitioning algorithm that substantially outperforms the existing state-of-the-art K-PM/LR algorithm for multi-way partitioning. both for optimizing local as well as global objectives. Experiments on the ISPD98 benchmark suite show that the partitionings produced by our scheme are on the average 15% to 23% better than those produced by the K-PM/LR algorithm, both in terms of the hyperedge cut as well as the (K - 1) metric. Furthermore, our algorithm is significantly faster, requiring 4 to 5 times less time than that required by K-PM/LR. 1 Introduction Hypergraph partitioning is an important problem with extensive application to many areas, including VLSI design 10, efficient storage of large databases on disks 14, and data mining 13. The problem is to partition the vertices of a hypergraph into k roughly equal parts, such that a certain objective function defined over the hyperedges is optimized. A commonly used obje...
%0 Conference Paper
%1 Karypis98multilevelk-way
%A Karypis, George
%A Kumar, Vipin
%B In Proceedings of the Design and Automation Conference
%D 1998
%K clustering community detection graph hypergraph partitioning
%P 343--348
%T Multilevel k-way Hypergraph Partitioning
%X In this paper, we present a new multilevel k-way hypergraph partitioning algorithm that substantially outperforms the existing state-of-the-art K-PM/LR algorithm for multi-way partitioning. both for optimizing local as well as global objectives. Experiments on the ISPD98 benchmark suite show that the partitionings produced by our scheme are on the average 15% to 23% better than those produced by the K-PM/LR algorithm, both in terms of the hyperedge cut as well as the (K - 1) metric. Furthermore, our algorithm is significantly faster, requiring 4 to 5 times less time than that required by K-PM/LR. 1 Introduction Hypergraph partitioning is an important problem with extensive application to many areas, including VLSI design 10, efficient storage of large databases on disks 14, and data mining 13. The problem is to partition the vertices of a hypergraph into k roughly equal parts, such that a certain objective function defined over the hyperedges is optimized. A commonly used obje...
@inproceedings{Karypis98multilevelk-way,
abstract = {In this paper, we present a new multilevel k-way hypergraph partitioning algorithm that substantially outperforms the existing state-of-the-art K-PM/LR algorithm for multi-way partitioning. both for optimizing local as well as global objectives. Experiments on the ISPD98 benchmark suite show that the partitionings produced by our scheme are on the average 15% to 23% better than those produced by the K-PM/LR algorithm, both in terms of the hyperedge cut as well as the (K - 1) metric. Furthermore, our algorithm is significantly faster, requiring 4 to 5 times less time than that required by K-PM/LR. 1 Introduction Hypergraph partitioning is an important problem with extensive application to many areas, including VLSI design [10], efficient storage of large databases on disks [14], and data mining [13]. The problem is to partition the vertices of a hypergraph into k roughly equal parts, such that a certain objective function defined over the hyperedges is optimized. A commonly used obje...},
added-at = {2009-06-22T10:07:47.000+0200},
author = {Karypis, George and Kumar, Vipin},
biburl = {https://www.bibsonomy.org/bibtex/2d63a73732f65ce10595e210cedda3bd1/folke},
booktitle = {In Proceedings of the Design and Automation Conference},
description = {CiteSeerX — Multilevel k-way Hypergraph Partitioning},
interhash = {413d89f472133bf5ff0671cccc818f55},
intrahash = {d63a73732f65ce10595e210cedda3bd1},
keywords = {clustering community detection graph hypergraph partitioning},
pages = {343--348},
timestamp = {2009-06-22T10:07:47.000+0200},
title = {Multilevel k-way Hypergraph Partitioning},
year = 1998
}