• Structural Engineering and Mechanics
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• 제14권6호
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• pp.625-647
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• 2002

Parallel execution of computational mechanics codes requires efficient mesh-partitioning techniques. These mesh-partitioning techniques divide the mesh into specified number of submeshes of approximately the same size and at the same time, minimise the interface nodes of the submeshes. This paper describes a new mesh partitioning technique, employing Genetic Algorithms. The proposed algorithm operates on the deduced graph (dual or nodal graph) of the given finite element mesh rather than directly on the mesh itself. The algorithm works by first constructing a coarse graph approximation using an automatic graph coarsening method. The coarse graph is partitioned and the results are interpolated onto the original graph to initialise an optimisation of the graph partition problem. In practice, hierarchy of (usually more than two) graphs are used to obtain the final graph partition. The proposed partitioning algorithm is applied to graphs derived from unstructured finite element meshes describing practical engineering problems and also several example graphs related to finite element meshes given in the literature. The test results indicate that the proposed GA based graph partitioning algorithm generates high quality partitions and are superior to spectral and multilevel graph partitioning algorithms.

• 한국음향학회지
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• 제30권2호
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• pp.80-85
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• 2011

Speaker change detection involves the identification of time indices of an audio stream, where the identity of the speaker changes. In this paper, we propose novel measures for the speaker change detection based on a graph-partitioning criterion over the pairwise distance matrix of feature-vector stream. Experiments on both synthetic and real-world data were performed and showed that the proposed approach yield promising results compared with the conventional statistical measures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권4호
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• pp.1188-1202
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• 2012

In this paper, we propose a new multi-scale connected coherence tree algorithm (MCCTA) by improving the connected coherence tree algorithm (CCTA). In contrast to many multi-scale image processing algorithms, MCCTA works on multiple scales space of an image and can adaptively change the parameters to capture the coarse and fine level details. Furthermore, we design a Multi-scale Connected Coherence Tree algorithm plus Spectral graph partitioning (MCCTSGP) by combining MCCTA and Spectral graph partitioning in to a new framework. Specifically, the graph nodes are the regions produced by CCTA and the image pixels, and the weights are the affinities between nodes. Then we run a spectral graph partitioning algorithm to partition on the graph which can consider the information both from pixels and regions to improve the quality of segments for providing image segmentation. The experimental results on Berkeley image database demonstrate the accuracy of our algorithm as compared to existing popular methods.

• 대한전자공학회논문지
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• 제27권7호
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• pp.1025-1032
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• 1990

We proposed a neural network energy function for the optimal graph partitioning and its optimization method using Boltzmann Machine. We composed a Boltzmann Machine with the proposed neural network energy function, and the simulation results show that we can obtain an optimal solution with the energy function parameters of A=50, B=5, c=14 and D=10, at the Boltzmann Machine parameters of To=80 and \ulcorner0.07 for a 6-node 3-partition problem. As a result, the proposed energy function and optimization parameters are proved to be feasible for the optimal graph partitioning.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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• pp.718-721
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• 2004

This paper introduces a novel method of partitioning a cyclic workflow graph into the subgraphs of acyclic flows. The way of iterative classification of nodes according to feedback structures and deriving subgraphs of acyclic flows is described with illustrative examples. The proposed method allows a cyclic workflow model to be analyzed further, if necessary, with several smaller subflows, which are all acyclic.

• 한국경영과학회지
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• 제19권3호
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• pp.219-233
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• 1994

A fixed k-coloring problem is introduced and dealt with by efficient heuristic algorithms. It is shown that the problem can be transformed into the graph partitioning problem. Initial coloring and improving methods are proposed for problems with and with and without the size restriction. Algorithm Move, LEE and OEE are developed by modifying the Kernighan-Lin's two way uniform partitioning procedure. The use of global information in the selection of the node and the color set made the proposed algorithms superior to the existing method. The computational result also shows that the superiority does not sacrifice the time demand of the proposed algorithms.

• 한국정보과학회논문지:데이타베이스
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• 제31권3호
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• pp.320-330
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• 2004

XML에 대한 질의 언어는 데이타 그래프 내의 경로를 이용하여 질의를 표현한다. 특히, 경로에 패턴 (예를 들어, 정규식)을 사용함으로써, 데이타의 구조를 정확히 알지 못하더라도 질의가 가능하도록 한다. 이때, 패턴을 이용하는 질의는 데이타 그래프의 탐색범위를 크게 넓히게 된다. 기존의 XML색인 기법은 질의의 탐색범위를 줄이기 위해 데이타 그래프 내의 서로 동일한 경로들을 하나로 묶어 작은 크기의 색인 그래프를 생성하는 방법을 이용한다. 하지만 이러한 색인들은 많은 경우 색인의 크기가 데이터 그래프의 크기만큼 증가하게 되어 질의의 탐색범위를 줄이지 못하고, 따라서 효율적인 질의 처리를 보장하지 못한다. 본 논문에서는 데이타 내에 존재하는 모든 경로를 분할(partitioning)하고 질의 처리 시 질의에 맞는 분할 영역을 빠르게 찾아낼 수 있는 색인 그래프를 제안한다. 본 논문에서 제안하는 색인 그래프는 데이터 그래프의 크기와 상관없이 색인 그래프의 크기를 조절할 수 있다. 따라서 색인 그래프의 크기를 작게 구성함으로써 색인 그래프 탐색 비용을 크게 줄일 수 있다. 본 논문에서는, 실험을 통해 기존의 그래프 기반색인 기법들보다 본 논문의 색인 기법이 보다 효율적임을 보이고 색인의 크기 변화에 따른 성능 변화에 대해 알아본다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2003년도 봄 학술발표논문집 Vol.30 No.1 (A)
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• pp.875-877
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• 2003

그래프 분할문제에서 대량의 그래프 데이터를 처리하는 것은 계산에서 걸리는 시간보다 파일 입출력을 수행하는 데 걸리는 시간의 비중이 크다. 본 논문은 수행 속도와 분할 성능에 있어서 우수한 그래프 분할 알고리즘 중 하나인 Multilevel Graph Partitioning에 대해 입출력 효율을 높일 수 있도록 확장하는 기법을 제안하고 그 구현에 대해 기술한다. 그래프를 컴퓨터의 가용 메모리를 기준으로 서브 그래프로 나누어 메모리 참조의 지역성이 향상되도록 기존의 Multilevel Graph Partitioning을 확장 하였다. 기존의 방식과 제안된 방식을 테스트 그래프들에 적용하여 그 수행시간을 비교한 결과 그래프 데이터의 크기가 컴퓨터의 주 메모리의 용량에 비해 어느 수준 이상으로 커지면서 제안된 알고리즘이 기존의 방식보다 수행시간에 있어서 좋은 결과를 보인다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2005년도 한국컴퓨터종합학술대회 논문집 Vol.32 No.1 (A)
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• pp.892-894
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• 2005

Graph partitioning provides an important tool for data clustering, but is an NP-hard combinatorial optimization problem. Spectral clustering where the clustering is performed by the eigen-decomposition of an affinity matrix [1,2]. This is a popular way of solving the graph partitioning problem. On the other hand, semidefinite relaxation, is an alternative way of relaxing combinatorial optimization. issuing to a convex optimization[4]. In this paper we present a semidefinite programming (SDP) approach to graph equi-partitioning for clustering and then we use eigen-decomposition to obtain an optimal partition set. Therefore, the method is referred to as semidefinite spectral clustering (SSC). Numerical experiments with several artificial and real data sets, demonstrate the useful behavior of our SSC. compared to existing spectral clustering methods.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
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• pp.529-532
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• 1988

This paper proposes a graph matching algorithm based on simulated annealing, which assures the globally optimal solution for circuit partitioning for the placement in the rectilinear region occurring as a result of the pre-placement of some macro cells, or onto the nonplanar surface in some military or space applications. The circuit graph ($G_{C}$) denoting the circuit topology is formed by a hierarchical bottom-up clustering of cells, while another graph called region graph ($G_{R}$) represents the geometry of a planar rectilinear region or a nonplanar surface for circuit placement. Finding the optimal many-to-one vertex mapping function from $G_{C}$ to $G_{R}$, such that the total mismatch cost between two graphs is minimal, is a combinatorial optimization problem which was solved in this work for various examples using simulated annealing.