• 대한수학회보
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• 제39권1호
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• pp.175-184
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• 2002

A Cellular embedding of a graph G into an orientable surface S can be considered as a cellular decomposition of S into 0-cells, 1-cells and 2-cells and vise versa, in which 0-cells and 1-cells form a graph G and this decomposition of S is called a map in S with underlying graph G. For a map M with underlying graph G, we define a natural rotation on the line graph of the graph G and we introduce the line map for M. we find that genus of the supporting surface of the line map for a map and we give a characterization for the line map to be embedded in the sphere. Moreover we show that the line map for any life of a map M is map-isomorphic to a lift of the line map for M.

• 대한수학회보
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• 제44권4호
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• pp.709-719
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• 2007

In [8], it is shown that the complete multipartite graph $K_{n(2t)}$ having n partite sets of size 2t, where $n{\geq}6\;and\;t{\geq}1$, has a decomposition into gregarious 6-cycles if $n{\equiv}0,1,3$ or 4 (mod 6). Here, a cycle is called gregarious if it has at most one vertex from any particular partite set. In this paper, when $n{\equiv}0$ or 3 (mod 6), another method using difference set is presented. Furthermore, when $n{\equiv}0$ (mod 6), the decomposition obtained in this paper is ${\infty}-circular$, in the sense that it is invariant under the mapping which keeps the partite set which is indexed by ${\infty}$ fixed and permutes the remaining partite sets cyclically.

• 전자공학회논문지
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• 제52권2호
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• pp.162-170
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• 2015

컴뮤트 타임 임베딩을 구현하려면 그래프 라플라시안 행렬의 고유값과 고유벡터를 구하여야 하는데, $o(n^3)$의 계산량이 요구되어 대용량 데이터에는 적용하기 어려운 문제가 있다. 이를 줄이기 위하여 표본화 과정을 통하여 크기가 줄어든 그래프 라플라시안 행렬에서 구한 다음, 원래의 고유값과 고유벡터를 근사화시키는 Nystr${\ddot{o}}$m 기법을 주로 채택한다. 이 과정에서 많은 오차가 발생하는데, 이를 개선하기 위하여 본 논문에서는 그래프 라플라시안 대신에 가중치 행렬을 표본화하고 이로부터 구한 고유값과 고유벡터를 그래프 라플라시안의 고유값과 고유벡터로 변환하는 기법을 이용하여 대용량 데이터로 구성된 스펙트럴 그래프를 근사적으로 컴뮤트 타임 임베딩하는 기법을 제안한다. 하지만, 이 방식도 스펙트럼 분해를 계산하여야 하므로 데이터의 크기가 증가하면 적용하기 어려운 문제가 발생한다. 이의 대안으로, 스펙트럼 분해를 계산하지 않고도 데이터 집합의 크기에 영향을 받지 않으면서 컴뮤트 타임을 근사적으로 계산하는 방식을 구현하고 이들의 특성을 실험적으로 분석한다.

• 제어로봇시스템학회논문지
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• 제16권1호
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• pp.48-52
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• 2010

In this paper, we introduce a new path planning algorithm which combines the merits of a visibility graph algorithm and an adaptive cell decomposition. We quantize a given map with empty cells, blocked cells, and mixed cells, then find the optimal path on the quantized map using a visibility graph algorithm. For reducing the number of the quantized cells we use the quad-tree technique which is used in an adaptive cell decomposition, and for improving the performance of the visibility checking in making a visibility graph we propose a new visibility checking method which uses the property of the quad-tree instead of the well-known rotational sweep-line algorithm. For the more efficient visibility checking, we propose two additional improvements for our suggested method. Both of them are used for reducing the visited cells in the quad-tree. The experiments for a performance comparison of our algorithm with other well-known algorithms show that our proposed method is superior to others.

• 한국정보처리학회논문지
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• 제6권3호
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• pp.571-579
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• 1999

A Macro-Star graph which has a star graph as a basic module has node symmetry, maximum fault tolerance, and hierarchical decomposition property. And, it is an interconnection network which improves a network cost against a star graph. A matrix star graph also has such good properties of a Macro-Star graph and is an interconnection network which has a lower network cost than a Maco-Star graph. In this paper, we propose a method to embed between a Macro-Star graph and a matrix star graph. We show that a Macro-Star graph MS(k, n) can be embedded into a matrix star graph MS\ulcorner with dilation 2. In addition, we show that a matrix star graph MS\ulcorner can be embedded into a Macro-Star graph MS(k,n+1) with dilation 4 and average dilation 3 or less as well. This result means that several algorithms developed in a star graph can be simulated in a matrix star graph with constant cost.

• 대한수학회논문집
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• 제30권3호
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• pp.313-325
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• 2015

An H-magic labeling in a H-decomposable graph G is a bijection $f:V(G){\cup}E(G){\rightarrow}\{1,2,{\cdots},p+q\}$ such that for every copy H in the decomposition, $\sum{_{{\upsilon}{\in}V(H)}}\;f(v)+\sum{_{e{\in}E(H)}}\;f(e)$ is constant. f is said to be H-V -super magic if f(V(G))={1,2,...,p}. In this paper, we prove that complete bipartite graphs $K_{n,n}$ are H-V -super magic decomposable where $$H{\sim_=}K_{1,n}$$ with $n{\geq}1$.

• 대한수학회지
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• 제45권6호
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• pp.1623-1634
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• 2008

The complete multipartite graph $K_{n(2t)}$ having n partite sets of size 2t, with $n\;{\geq}\;6$ and $t\;{\geq}\;1$, is shown to have a decomposition into gregarious 6-cycles, that is, the cycles which have at most one vertex from any particular partite set. Complete sets of differences of numbers in ${\mathbb{Z}}_n$ are used to produce starter cycles and obtain other cycles by rotating the cycles around the n-gon of the partite sets.

• East Asian mathematical journal
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• 제26권5호
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• pp.655-670
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• 2010

The complete multipartite graph $K_{n(m)}$ with n $ {\geq}$ 4 partite sets of size m is shown to have a decomposition into 4-cycles in such a way that vertices of each cycle belong to distinct partite sets of $K_{n(m)}$, if 4 divides the number of edges. Such cycles are called gregarious, and were introduced by Billington and Hoffman ([2]) and redefined in [3]. We independently came up with the result of [3] by using a difference set method, and improved the result so that the composition is circulant, in the sense that it is invariant under the cyclic permutation of partite sets. The composition is then used to construct gregarious 4-cycle decompositions when one partite set of the graph has different cardinality than that of others. Some results on joins of decomposable complete multipartite graphs are also presented.

• 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.

• 정보처리학회논문지B
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• 제10B권3호
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• pp.265-272
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• 2003

본 논문에서는 동적으로 변화하는 대용량의 데이터베이스로부터 보다 현실적인 데이터 마이닝의 수행을 가능케 하기 위하여 기존의 직사각형분해 알고리즘을 개선한 새로운 알고리즘을 제안한다. 새로운 알고리즘은 이진 행렬을 이분(bipartite) 그래프로 변환하고, 변환된 이분 그래프에서 이분클리크(biclique)를 찾음으로써 직사각형 분해를 수행한다 제안된 알고리즘은 새롭게 유도된 수학적 정리들을 바탕으로 출발하였으며, 복잡도 분석을 통하여 그 효율성을 보이고, 기존의 분류 방법론과의 비교를 통하여 제안된 방법론이 규칙의 수와 분류율면에서 우수함을 보인다.