• Title/Summary/Keyword: Pruning Search Space

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Improved approach of calculating the same shape in graph mining (그래프 마이닝에서 그래프 동형판단연산의 향상기법)

  • No, Young-Sang;Yun, Un-Il;Kim, Myung-Jun
    • Journal of the Korea Society of Computer and Information
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    • v.14 no.10
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    • pp.251-258
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    • 2009
  • Data mining is a method that extract useful knowledges from huge size of data. Recently, a focussing research part of data mining is to find interesting patterns in graph databases. More efficient methods have been proposed in graph mining. However, graph analysis methods are in NP-hard problem. Graph pattern mining based on pattern growth method is to find complete set of patterns satisfying certain property through extending graph pattern edge by edge with avoiding generation of duplicated patterns. This paper suggests an efficient approach of reducing computing time of pattern growth method through pattern growth's property that similar patterns cause similar tasks. we suggest pruning methods which reduce search space. Based on extensive performance study, we discuss the results and the future works.

Content Based Image Retrieval using 8AB Representation of Spatial Relations between Objects (객체 위치 관계의 8AB 표현을 이용한 내용 기반 영상 검색 기법)

  • Joo, Chan-Hye;Chung, Chin-Wan;Park, Ho-Hyun;Lee, Seok-Lyong;Kim, Sang-Hee
    • Journal of KIISE:Databases
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    • v.34 no.4
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    • pp.304-314
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    • 2007
  • Content Based Image Retrieval (CBIR) is to store and retrieve images using the feature description of image contents. In order to support more accurate image retrieval, it has become necessary to develop features that can effectively describe image contents. The commonly used low-level features, such as color, texture, and shape features may not be directly mapped to human visual perception. In addition, such features cannot effectively describe a single image that contains multiple objects of interest. As a result, the research on feature descriptions has shifted to focus on higher-level features, which support representations more similar to human visual perception like spatial relationships between objects. Nevertheless, the prior works on the representation of spatial relations still have shortcomings, particularly with respect to supporting rotational invariance, Rotational invariance is a key requirement for a feature description to provide robust and accurate retrieval of images. This paper proposes a high-level feature named 8AB (8 Angular Bin) that effectively describes the spatial relations of objects in an image while providing rotational invariance. With this representation, a similarity calculation and a retrieval technique are also proposed. In addition, this paper proposes a search-space pruning technique, which supports efficient image retrieval using the 8AB feature. The 8AB feature is incorporated into a CBIR system, and the experiments over both real and synthetic image sets show the effectiveness of 8AB as a high-level feature and the efficiency of the pruning technique.

Analysis of Threshold Voltage Characteristics for FinFET Using Three Dimension Poisson's Equation (3차원 포아송방정식을 이용한 FinFET의 문턱전압특성분석)

  • Jung, Hak-Kee
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.13 no.11
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    • pp.2373-2377
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    • 2009
  • In this paper, the threshold voltage characteristics have been analyzed using three dimensional Poisson's equation for FinFET. The FinFET is extensively been studing since it can reduce the short channel effects as the nano device. We have presented the short channel effects such as subthreshold swing and threshold voltage for PinFET, using the analytical three dimensional Poisson's equation. We have analyzed for channel length, thickness and width to consider the structural characteristics for FinFET. Using this model, the subthreshold swing and threshold voltage have been analyzed for FinFET since the potential and transport model of this analytical three dimensional Poisson's equation is verified as comparing with those of the numerical three dimensional Poisson's equation.

Implementation and Evaluation of Path-Finding Algorithm using Abstract Graphs (추상 그래프를 활용한 경로 탐색 알고리즘의 구현 및 성능 평가)

  • Kim, Ji-Soo;Lee, Ji-Wan;Cho, Dae-Soo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.13 no.11
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    • pp.2367-2372
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    • 2009
  • Recently, Many studies have been progressing to path-finding adapted dynamic information on the Terminal Based Navigation System(TBNS). The algorithms proposed are based on $A^*$ algorithm. Path-finding algorithms which use heuristic function may occur a problem of the increase of exploring cost. Path-finding with an abstract graph which expresses real road network as a simple graph is proposed for reducing dependency of heuristic and exploring cost. In this paper, two abstract graph that are different method of construction, Homogeneous Node merging($AG^H$) and Connected Node Merging($AG^C$), are implemented. In result of evaluation of performance, $AG^C$ has better performance than $AG^H$ at construction cost and the number of node access but $AG^C$ has worse performance than AGH at exploring cost.

A Path Finding Algorithm based on an Abstract Graph Created by Homogeneous Node Elimination Technique (동일 특성 노드 제거를 통한 추상 그래프 기반의 경로 탐색 알고리즘)

  • Kim, Ji-Soo;Lee, Ji-Wan;Cho, Dea-Soo
    • Journal of Korea Spatial Information System Society
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    • v.11 no.4
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    • pp.39-46
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    • 2009
  • Generally, Path-finding algorithms which use heuristic function may occur a problem of the increase of exploring cost in case of that there is no way determined by heuristic function or there are 2 way more which have almost same cost. In this paper, we propose an abstract graph for path-finding with dynamic information. The abstract graph is a simple graph as real road network is abstracted. The abstract graph is created by fixed-size cells and real road network. Path-finding with the abstract graph is composed of two step searching, path-finding on the abstract graph and on the real road network. We performed path-finding algorithm with the abstract graph against A* algorithm based on fixed-size cells on road network that consists of 106,254 edges. In result of evaluation of performance, cost of exploring in path-finding with the abstract graph is about 3~30% less than A* algorithm based on fixed-size cells. Quality of path in path-finding with the abstract graph is, However, about 1.5~6.6% more than A* algorithm based on fixed-size cells because edges eliminated are not candidates for path-finding.

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Implementation and Evaluation of Path-Finding Algorithm using Abstract Graphs (추상 그래프를 활용한 경로 탐색 알고리즘의 구현 및 성능 평가)

  • Kim, Ji-Soo;Lee, Ji-wan;Cho, Dae-Soo
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2009.10a
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    • pp.245-248
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    • 2009
  • Recently, Many studies have been progressing to path-finding with dynamic information on the Terminal Based Navigation System(TBNS). However, the most of existing algorithms are based on $A{\ast}$ algorithm. Path-finding algorithms which use heuristic function may occur a problem of the increase of exploring cost in case of that there is no way determined by heuristic function or there are 2 way more which have almost same cost. In this paper, two abstract graph(AG) that are different method of construction, Homogeneous Node merging($AG^H$) and Connected Node Merging($AG^C$), are implemented. The abstract graph is a simple graph of real road network. The method of using the abstract graph is proposed for reducing dependency of heuristic and exploring cost. In result of evaluation of performance, $AG^C$ has better performance than $AG^H$ at construction cost but $AG^C$ has worse performance than $AG^H$ exploring cost.

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Efficient Processing of k-Farthest Neighbor Queries for Road Networks

  • Kim, Taelee;Cho, Hyung-Ju;Hong, Hee Ju;Nam, Hyogeun;Cho, Hyejun;Do, Gyung Yoon;Jeon, Pilkyu
    • Journal of the Korea Society of Computer and Information
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    • v.24 no.10
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    • pp.79-89
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    • 2019
  • While most research focuses on the k-nearest neighbors (kNN) queries in the database community, an important type of proximity queries called k-farthest neighbors (kFN) queries has not received much attention. This paper addresses the problem of finding the k-farthest neighbors in road networks. Given a positive integer k, a query object q, and a set of data points P, a kFN query returns k data objects farthest from the query object q. Little attention has been paid to processing kFN queries in road networks. The challenge of processing kFN queries in road networks is reducing the number of network distance computations, which is the most prominent difference between a road network and a Euclidean space. In this study, we propose an efficient algorithm called FANS for k-FArthest Neighbor Search in road networks. We present a shared computation strategy to avoid redundant computation of the distances between a query object and data objects. We also present effective pruning techniques based on the maximum distance from a query object to data segments. Finally, we demonstrate the efficiency and scalability of our proposed solution with extensive experiments using real-world roadmaps.